From 6951c3aa7cd4557115835e86d7052b686236434f Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Wed, 22 Nov 2023 19:48:02 +0000 Subject: [PATCH 001/130] add first draft to solve #70 --- episodes/introduction.Rmd | 140 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 140 insertions(+) create mode 100644 episodes/introduction.Rmd diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd new file mode 100644 index 00000000..78a47980 --- /dev/null +++ b/episodes/introduction.Rmd @@ -0,0 +1,140 @@ +--- +title: 'Introduction' +teaching: 10 +exercises: 2 +editor_options: + chunk_output_type: console +--- + +:::::::::::::::::::::::::::::::::::::: questions + +- Why to use R packages for Outbreak analytics? +- What can we do to analyse our outbreak data? +- How can I start doing Outbreak analytics with R? + +:::::::::::::::::::::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::: objectives + +- Explain our vision on the need to outbreak analytics R packages. +- Share our strategy to incorporate R packages into an outbreak analytics pipeline. +- Define our plan to incorporate practical solutions and theoretical concepts on outbreak analytics. + +:::::::::::::::::::::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::: prereq + +## Prerequisites + +List (and hyperlink) the lessons/packages which need to be covered before this lesson + +```r +install_version( + package = "EpiNow2", version = "1.4.0", + repos = "http://cran.us.r-project.org" +) +``` + +::::::::::::::::::::::::::::::::: + + +## Introduction + +_write about the Reproduction number (in a motivational way)_ + +Packages are handy tools to reuse code, maintenance, less error prone data analysis steps. + +The `{EpiNow2}` provide us with a a three-steps solution for this task! + +```{r,warning=FALSE} +library(EpiNow2) +``` + + +## First, get your data + +A data frame of observation + +```{r} +example_confirmed +``` + +## Then, set the parameters + +```{r} +incubation_period_fixed <- dist_spec( + mean = 4, sd = 2, + max = 20, distribution = "gamma" +) + +reporting_delay_fixed <- dist_spec( + mean = convert_to_logmean(2, 1), + sd = convert_to_logsd(2, 1), + max = 10, distribution = "lognormal" +) + +generation_time_fixed <- dist_spec( + mean = 3.6, sd = 3.1, + max = 20, distribution = "lognormal" +) +``` + +## Let's calculate R! + +```{r,echo=FALSE} +setup_default_logging(logs = NULL) +``` + +```{r,message=FALSE,warning=FALSE} +epinow_estimates <- epinow( + # cases + reported_cases = example_confirmed[1:60], + # delays + generation_time = generation_time_opts(generation_time_fixed), + delays = delay_opts(incubation_period_fixed + reporting_delay_fixed), + # computation options + stan = stan_opts(cores = 4, samples = 10, chains = 2, + control = list(adapt_delta = 0.99)), + verbose = interactive() +) +``` + +```{r} +base::plot(epinow_estimates) +``` + + +## The problem! + +However, doing this in real life is not as easy as this example! + +Data analysis involves dealing with inputs problems. + +- Read your linelist +- Clean your linelist +- Validate your linelist +- Read parameters from literature + +Also you can usethis R outputs as inputs for other tasks. + +- Forecast cases +- Estimate severity +- Simulate transmission scenarios +- Compare interventions + +At Epiverse-TRACE we are creating packages that complement the current landscape filling gaps of epi-specific challenges in response to outbreaks. + +## How to? + +In first set of episodes we are going to deal with each of these task previous to the _Quantify transmission_ task. These preliminary task are the __Early tasks__. Then we are going to get deeper into the _Quantify transmission_ task, which is within the __Middle tasks__, and later ones in the pipeline called and __Late tasks__. + +![An overview of the tutorial task to cover.](https://epiverse-trace.github.io/task_pipeline-minimal.svg) + +::::::::::::::::::::::::::::::::::::: keypoints + +- Our vision is to have pipelines of R packages for outbreak analytics. +- Our strategy is to create interconnected tasks to get public health relevant outputs. +- Our plan is to introduce about package solutions and theory bits for each of the tasks in the outbreak analytics pipeline. + +:::::::::::::::::::::::::::::::::::::::::::::::: + From 70c44832d330423a387b13c50027ff4715229c1e Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Wed, 22 Nov 2023 19:50:29 +0000 Subject: [PATCH 002/130] add introduction episode for lesson_build --- config.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/config.yaml b/config.yaml index 1e8b76f1..02861823 100644 --- a/config.yaml +++ b/config.yaml @@ -59,6 +59,7 @@ contact: 'andree.valle-campos@lshtm.ac.uk' #FIXME # Order of episodes in your lesson episodes: +- introduction.Rmd - quantify-transmissibility.Rmd - create-forecast.Rmd - simulating-transmission.Rmd From 24b131cde683018d22308acfe46a2c490cb51557 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Wed, 22 Nov 2023 20:02:57 +0000 Subject: [PATCH 003/130] fix writing of objectives --- episodes/introduction.Rmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 78a47980..ae2e1a55 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -16,9 +16,9 @@ editor_options: ::::::::::::::::::::::::::::::::::::: objectives -- Explain our vision on the need to outbreak analytics R packages. -- Share our strategy to incorporate R packages into an outbreak analytics pipeline. -- Define our plan to incorporate practical solutions and theoretical concepts on outbreak analytics. +- Explain our vision on the need of outbreak analytics R packages. +- Share our strategy to create R packages into an outbreak analytics pipeline. +- Define our plan to start your learning path in outbreak analytics with R. :::::::::::::::::::::::::::::::::::::::::::::::: From deeae9264b23a82b068d040d7ebe9c44d24cff50 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Wed, 22 Nov 2023 20:03:33 +0000 Subject: [PATCH 004/130] add structure of questions to last two sections --- episodes/introduction.Rmd | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index ae2e1a55..61d4f42b 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -122,13 +122,16 @@ Also you can usethis R outputs as inputs for other tasks. - Simulate transmission scenarios - Compare interventions +## What can we do? + At Epiverse-TRACE we are creating packages that complement the current landscape filling gaps of epi-specific challenges in response to outbreaks. -## How to? +![An overview of the tutorial task to cover.](https://epiverse-trace.github.io/task_pipeline-minimal.svg) -In first set of episodes we are going to deal with each of these task previous to the _Quantify transmission_ task. These preliminary task are the __Early tasks__. Then we are going to get deeper into the _Quantify transmission_ task, which is within the __Middle tasks__, and later ones in the pipeline called and __Late tasks__. -![An overview of the tutorial task to cover.](https://epiverse-trace.github.io/task_pipeline-minimal.svg) +## How can we start? + +In first set of episodes we are going to deal with each of these task previous to the _Quantify transmission_ task. These preliminary task are the __Early tasks__. Then we are going to get deeper into the _Quantify transmission_ task, which is within the __Middle tasks__, and later ones in the pipeline called and __Late tasks__. ::::::::::::::::::::::::::::::::::::: keypoints From 8ca32486dcb281b0d4063c1095b1c668854e8c74 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Wed, 22 Nov 2023 20:31:19 +0000 Subject: [PATCH 005/130] remove temporally setup logs chunk --- episodes/introduction.Rmd | 4 ---- 1 file changed, 4 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 61d4f42b..bceb2f91 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -81,10 +81,6 @@ generation_time_fixed <- dist_spec( ## Let's calculate R! -```{r,echo=FALSE} -setup_default_logging(logs = NULL) -``` - ```{r,message=FALSE,warning=FALSE} epinow_estimates <- epinow( # cases From eec8bb2afa693949615c703806004406d3bda312 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Wed, 22 Nov 2023 21:18:52 +0000 Subject: [PATCH 006/130] reduce object names and visible arguments --- episodes/introduction.Rmd | 23 +++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index bceb2f91..dddcb000 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -62,18 +62,18 @@ example_confirmed ## Then, set the parameters ```{r} -incubation_period_fixed <- dist_spec( +incubation_period <- dist_spec( mean = 4, sd = 2, max = 20, distribution = "gamma" ) -reporting_delay_fixed <- dist_spec( +reporting_delay <- dist_spec( mean = convert_to_logmean(2, 1), sd = convert_to_logsd(2, 1), max = 10, distribution = "lognormal" ) -generation_time_fixed <- dist_spec( +generation_time <- dist_spec( mean = 3.6, sd = 3.1, max = 20, distribution = "lognormal" ) @@ -81,17 +81,20 @@ generation_time_fixed <- dist_spec( ## Let's calculate R! -```{r,message=FALSE,warning=FALSE} +```{r,message=FALSE,warning=FALSE,cache=TRUE} epinow_estimates <- epinow( # cases reported_cases = example_confirmed[1:60], # delays - generation_time = generation_time_opts(generation_time_fixed), - delays = delay_opts(incubation_period_fixed + reporting_delay_fixed), - # computation options - stan = stan_opts(cores = 4, samples = 10, chains = 2, - control = list(adapt_delta = 0.99)), - verbose = interactive() + generation_time = generation_time_opts(generation_time), + delays = delay_opts( + incubation_period + reporting_delay + ), + # computation + stan = stan_opts( + cores = 4, samples = 1000, chains = 2, + control = list(adapt_delta = 0.99) + ) ) ``` From b373ec7e934878faa54074ea009ddefd3dd1a967 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Wed, 22 Nov 2023 21:22:32 +0000 Subject: [PATCH 007/130] change heading level --- episodes/introduction.Rmd | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index dddcb000..c16e1b91 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -44,6 +44,8 @@ _write about the Reproduction number (in a motivational way)_ Packages are handy tools to reuse code, maintenance, less error prone data analysis steps. +## Quantify transmission + The `{EpiNow2}` provide us with a a three-steps solution for this task! ```{r,warning=FALSE} @@ -51,7 +53,7 @@ library(EpiNow2) ``` -## First, get your data +### First, get your data A data frame of observation @@ -59,7 +61,7 @@ A data frame of observation example_confirmed ``` -## Then, set the parameters +### Then, set the parameters ```{r} incubation_period <- dist_spec( @@ -79,7 +81,7 @@ generation_time <- dist_spec( ) ``` -## Let's calculate R! +### Let's calculate R! ```{r,message=FALSE,warning=FALSE,cache=TRUE} epinow_estimates <- epinow( From 1049801b8ab6ff93e1e19a9938799fe4051988f2 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 23 Nov 2023 00:57:38 +0000 Subject: [PATCH 008/130] replace episode title --- episodes/introduction.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index c16e1b91..48fd4cc5 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -1,5 +1,5 @@ --- -title: 'Introduction' +title: 'Outbreak analytics pipelines' teaching: 10 exercises: 2 editor_options: From dd4f68136a4a12e76ba03ee75ca715b701182710 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 23 Nov 2023 11:25:40 +0000 Subject: [PATCH 009/130] add template and questions for read data episode --- episodes/read-data.Rmd | 113 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 113 insertions(+) create mode 100644 episodes/read-data.Rmd diff --git a/episodes/read-data.Rmd b/episodes/read-data.Rmd new file mode 100644 index 00000000..426f5df2 --- /dev/null +++ b/episodes/read-data.Rmd @@ -0,0 +1,113 @@ +--- +title: 'read-data' +teaching: 10 +exercises: 2 +--- + +:::::::::::::::::::::::::::::::::::::: questions + +- how many different data formats can I read? +- why should I have to validated a linelist? +- how to check for age and date variables? + +:::::::::::::::::::::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::: objectives + +- Explain how to use markdown with the new lesson template +- Demonstrate how to include pieces of code, figures, and nested challenge blocks + +:::::::::::::::::::::::::::::::::::::::::::::::: + +## Introduction + +This is a lesson created via The Carpentries Workbench. It is written in +[Pandoc-flavored Markdown][pandoc] for static files (with extension `.md`) and +[R Markdown][r-markdown] for dynamic files that can render code into output +(with extension `.Rmd`). Please refer to the [Introduction to The Carpentries +Workbench][carpentries-workbench] for full documentation. + +What you need to know is that there are three sections required for a valid +Carpentries lesson template: + + 1. `questions` are displayed at the beginning of the episode to prime the + learner for the content. + 2. `objectives` are the learning objectives for an episode displayed with + the questions. + 3. `keypoints` are displayed at the end of the episode to reinforce the + objectives. + +:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: instructor + +Inline instructor notes can help inform instructors of timing challenges +associated with the lessons. They appear in the "Instructor View" + +:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::: challenge + +## Challenge 1: Can you do it? + +What is the output of this command? + +```r +paste("This", "new", "lesson", "looks", "good") +``` + +:::::::::::::::::::::::: solution + +## Output + +```output +[1] "This new lesson looks good" +``` + +::::::::::::::::::::::::::::::::: + + +## Challenge 2: how do you nest solutions within challenge blocks? + +:::::::::::::::::::::::: solution + +You can add a line with at least three colons and a `solution` tag. + +::::::::::::::::::::::::::::::::: +:::::::::::::::::::::::::::::::::::::::::::::::: + +## Figures + +You can include figures generated from R Markdown: + +```{r pyramid, fig.alt = "pie chart illusion of a pyramid", fig.cap = "Sun arise each and every morning"} +pie( + c(Sky = 78, "Sunny side of pyramid" = 17, "Shady side of pyramid" = 5), + init.angle = 315, + col = c("deepskyblue", "yellow", "yellow3"), + border = FALSE +) +``` +Or you can use pandoc markdown for static figures with the following syntax: + +`![optional caption that appears below the figure](figure url){alt='alt text for +accessibility purposes'}` + +![You belong in The Carpentries!](https://raw.githubusercontent.com/carpentries/logo/master/Badge_Carpentries.svg){alt='Blue Carpentries hex person logo with no text.'} + +## Math + +One of our episodes contains $\LaTeX$ equations when describing how to create +dynamic reports with {knitr}, so we now use mathjax to describe this: + +`$\alpha = \dfrac{1}{(1 - \beta)^2}$` becomes: $\alpha = \dfrac{1}{(1 - \beta)^2}$ + +Cool, right? + +::::::::::::::::::::::::::::::::::::: keypoints + +- Use `.md` files for episodes when you want static content +- Use `.Rmd` files for episodes when you need to generate output +- Run `sandpaper::check_lesson()` to identify any issues with your lesson +- Run `sandpaper::build_lesson()` to preview your lesson locally + +:::::::::::::::::::::::::::::::::::::::::::::::: + From f5943989f41a840a4eb3b4345cc69725d10ccaaf Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 23 Nov 2023 11:26:26 +0000 Subject: [PATCH 010/130] add draft chunk for discussion or challenge --- episodes/introduction.Rmd | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 48fd4cc5..01503e14 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -115,6 +115,7 @@ Data analysis involves dealing with inputs problems. - Clean your linelist - Validate your linelist - Read parameters from literature +- Describe the outbreak in person, place, and time Also you can usethis R outputs as inputs for other tasks. @@ -123,6 +124,39 @@ Also you can usethis R outputs as inputs for other tasks. - Simulate transmission scenarios - Compare interventions +::::::::::::::::::::::::::::::::: challenge + +### Your last outbreak response + +Remember your last experience with outbreak data and reflect in these questions: + +- What data sources your needed to understand the outbreak? +- How did you get accessed to that data? +- Is that analysis pipeline you follow reusable for your next response? + +::::::::::::::::: hint + +### HINT (`hint`) + +HINT accordions are created with the `hint` tag and nested within +`challenge` callouts. These offer more context for the challenge that can help +the learner find the solution. + +:::::::::::::::::::::: + +::::::::::::::::: solution + +### SOLUTION (`solution`) + +SOLUTION accordions are created with the `solution` tag and nested within +`challenge` callouts. These provide the learners with the solution to the +challenge so they can confirm their answers. + +:::::::::::::::::::::::::: + + +::::::::::::::::::::::::::::::::::::::::::: + ## What can we do? At Epiverse-TRACE we are creating packages that complement the current landscape filling gaps of epi-specific challenges in response to outbreaks. From 4fa5dfc6f577abb5e5d2d33c7316762dfdf05d54 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 23 Nov 2023 11:26:46 +0000 Subject: [PATCH 011/130] add template and questions for describe data episode --- episodes/describe-data.Rmd | 118 +++++++++++++++++++++++++++++++++++++ 1 file changed, 118 insertions(+) create mode 100644 episodes/describe-data.Rmd diff --git a/episodes/describe-data.Rmd b/episodes/describe-data.Rmd new file mode 100644 index 00000000..53a7dc27 --- /dev/null +++ b/episodes/describe-data.Rmd @@ -0,0 +1,118 @@ +--- +title: 'describe-data' +teaching: 10 +exercises: 2 +--- + +:::::::::::::::::::::::::::::::::::::: questions + +- how to obtain delay distributions from a literature systematic review? +- what is the natural history of the disease? +- how long should contacts be followed? +- what is the required duration of contact tracing? +- how long should cases be isolated to reduce transmission? + +- what are the epidemiological characteristics of the infection? +- what is the demographic distribution of cases? + +:::::::::::::::::::::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::: objectives + +- Explain how to use markdown with the new lesson template +- Demonstrate how to include pieces of code, figures, and nested challenge blocks + +:::::::::::::::::::::::::::::::::::::::::::::::: + +## Introduction + +This is a lesson created via The Carpentries Workbench. It is written in +[Pandoc-flavored Markdown][pandoc] for static files (with extension `.md`) and +[R Markdown][r-markdown] for dynamic files that can render code into output +(with extension `.Rmd`). Please refer to the [Introduction to The Carpentries +Workbench][carpentries-workbench] for full documentation. + +What you need to know is that there are three sections required for a valid +Carpentries lesson template: + + 1. `questions` are displayed at the beginning of the episode to prime the + learner for the content. + 2. `objectives` are the learning objectives for an episode displayed with + the questions. + 3. `keypoints` are displayed at the end of the episode to reinforce the + objectives. + +:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: instructor + +Inline instructor notes can help inform instructors of timing challenges +associated with the lessons. They appear in the "Instructor View" + +:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::: challenge + +## Challenge 1: Can you do it? + +What is the output of this command? + +```r +paste("This", "new", "lesson", "looks", "good") +``` + +:::::::::::::::::::::::: solution + +## Output + +```output +[1] "This new lesson looks good" +``` + +::::::::::::::::::::::::::::::::: + + +## Challenge 2: how do you nest solutions within challenge blocks? + +:::::::::::::::::::::::: solution + +You can add a line with at least three colons and a `solution` tag. + +::::::::::::::::::::::::::::::::: +:::::::::::::::::::::::::::::::::::::::::::::::: + +## Figures + +You can include figures generated from R Markdown: + +```{r pyramid, fig.alt = "pie chart illusion of a pyramid", fig.cap = "Sun arise each and every morning"} +pie( + c(Sky = 78, "Sunny side of pyramid" = 17, "Shady side of pyramid" = 5), + init.angle = 315, + col = c("deepskyblue", "yellow", "yellow3"), + border = FALSE +) +``` +Or you can use pandoc markdown for static figures with the following syntax: + +`![optional caption that appears below the figure](figure url){alt='alt text for +accessibility purposes'}` + +![You belong in The Carpentries!](https://raw.githubusercontent.com/carpentries/logo/master/Badge_Carpentries.svg){alt='Blue Carpentries hex person logo with no text.'} + +## Math + +One of our episodes contains $\LaTeX$ equations when describing how to create +dynamic reports with {knitr}, so we now use mathjax to describe this: + +`$\alpha = \dfrac{1}{(1 - \beta)^2}$` becomes: $\alpha = \dfrac{1}{(1 - \beta)^2}$ + +Cool, right? + +::::::::::::::::::::::::::::::::::::: keypoints + +- Use `.md` files for episodes when you want static content +- Use `.Rmd` files for episodes when you need to generate output +- Run `sandpaper::check_lesson()` to identify any issues with your lesson +- Run `sandpaper::build_lesson()` to preview your lesson locally + +:::::::::::::::::::::::::::::::::::::::::::::::: + From 6e10b656fffbe53b7ce8bcdab54dba1b5dd55cd0 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 23 Nov 2023 11:27:30 +0000 Subject: [PATCH 012/130] add new episodes for build review --- config.yaml | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/config.yaml b/config.yaml index 02861823..93ee5c98 100644 --- a/config.yaml +++ b/config.yaml @@ -58,8 +58,10 @@ contact: 'andree.valle-campos@lshtm.ac.uk' #FIXME # - another-learner.md # Order of episodes in your lesson -episodes: +episodes: - introduction.Rmd +- read-data.Rmd +- describe-data.Rmd - quantify-transmissibility.Rmd - create-forecast.Rmd - simulating-transmission.Rmd @@ -68,18 +70,20 @@ episodes: - compare-interventions.Rmd # Information for Learners -learners: +learners: # Information for Instructors -instructors: +instructors: # Learner Profiles -profiles: +profiles: # Customisation --------------------------------------------- # # This space below is where custom yaml items (e.g. pinning # sandpaper and varnish versions) should live + + varnish: epiverse-trace/varnish@epiversetheme # this is carpentries/sandpaper#533 in our fork so we can keep it up to date with main sandpaper: epiverse-trace/sandpaper@patch-renv-github-bug From d0a40cc017e34d00f95be64f1c39984b1b3963a9 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 23 Nov 2023 15:59:34 +0000 Subject: [PATCH 013/130] update heading titles --- episodes/introduction.Rmd | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 01503e14..9fbe38c2 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -61,7 +61,9 @@ A data frame of observation example_confirmed ``` -### Then, set the parameters +### Then, set the delay distributions + +The natural history of a disease influence the transmission ```{r} incubation_period <- dist_spec( @@ -81,17 +83,20 @@ generation_time <- dist_spec( ) ``` -### Let's calculate R! +### Let's calculate the reproductive number! ```{r,message=FALSE,warning=FALSE,cache=TRUE} epinow_estimates <- epinow( + # cases reported_cases = example_confirmed[1:60], + # delays generation_time = generation_time_opts(generation_time), delays = delay_opts( incubation_period + reporting_delay ), + # computation stan = stan_opts( cores = 4, samples = 1000, chains = 2, From a2c87faa79a4a2455b85bdaff998ba7c2b103e2d Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 23 Nov 2023 16:00:11 +0000 Subject: [PATCH 014/130] add paragraphs to the what and how --- episodes/introduction.Rmd | 44 ++++++++++++++------------------------- 1 file changed, 16 insertions(+), 28 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 9fbe38c2..1002a584 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -109,17 +109,16 @@ epinow_estimates <- epinow( base::plot(epinow_estimates) ``` - ## The problem! However, doing this in real life is not as easy as this example! Data analysis involves dealing with inputs problems. +- Read parameters from literature - Read your linelist - Clean your linelist - Validate your linelist -- Read parameters from literature - Describe the outbreak in person, place, and time Also you can usethis R outputs as inputs for other tasks. @@ -129,49 +128,38 @@ Also you can usethis R outputs as inputs for other tasks. - Simulate transmission scenarios - Compare interventions -::::::::::::::::::::::::::::::::: challenge +::::::::::::::::::::::::::::::::: discussion -### Your last outbreak response +### Questions to think about Remember your last experience with outbreak data and reflect in these questions: -- What data sources your needed to understand the outbreak? +- What data sources you needed to understand the outbreak? - How did you get accessed to that data? -- Is that analysis pipeline you follow reusable for your next response? - -::::::::::::::::: hint - -### HINT (`hint`) - -HINT accordions are created with the `hint` tag and nested within -`challenge` callouts. These offer more context for the challenge that can help -the learner find the solution. - -:::::::::::::::::::::: +- Is that analysis pipeline you followed reusable for a next response? -::::::::::::::::: solution +Pair up and share your experiences. -### SOLUTION (`solution`) +::::::::::::::::::::::::::::::::::::::::::: -SOLUTION accordions are created with the `solution` tag and nested within -`challenge` callouts. These provide the learners with the solution to the -challenge so they can confirm their answers. +## What can we do? -:::::::::::::::::::::::::: +One strategy is to incorporate into your traditional analysis pipeline new R packages that fill gaps of this epi- specific challenges in response to outbreaks. +Epiverse-TRACE aim is to provide a software ecosystem for outbreak analytics. This includes to support the development of software pieces, make the existing ones interoperable for the user experience, and stimulate a community of practice. -::::::::::::::::::::::::::::::::::::::::::: +![An overview of the tutorial task to cover.](https://epiverse-trace.github.io/task_pipeline-minimal.svg) -## What can we do? -At Epiverse-TRACE we are creating packages that complement the current landscape filling gaps of epi-specific challenges in response to outbreaks. +## How can I start? -![An overview of the tutorial task to cover.](https://epiverse-trace.github.io/task_pipeline-minimal.svg) +Our plan for these tutorials is to introduce key solutions from packages in the outbreak analytics pipeline, plus the required theory concepts to interpret modelling outputs and make rigorous conclusions. +- In the first set of episodes, we are going to learn to apply handy package solutions to deal with each of the task previous to _Quantify transmission_ of an outbreak. These preliminary task are the __Early tasks__. -## How can we start? +- Then, we are going to get deeper into the packages and required theory concepts to _Quantify transmission_ and other ones immediately related with it, all of them named as __Middle tasks__. -In first set of episodes we are going to deal with each of these task previous to the _Quantify transmission_ task. These preliminary task are the __Early tasks__. Then we are going to get deeper into the _Quantify transmission_ task, which is within the __Middle tasks__, and later ones in the pipeline called and __Late tasks__. +- Lastly, we will use _Quantify transmission_ data outputs to feed complementary tasks in the pipeline called and __Late tasks__. ::::::::::::::::::::::::::::::::::::: keypoints From d8c5edd946aedac524e04e0429b298f567d0a757 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 23 Nov 2023 16:00:37 +0000 Subject: [PATCH 015/130] add keypoints from episode --- episodes/read-data.Rmd | 90 ++++-------------------------------------- 1 file changed, 7 insertions(+), 83 deletions(-) diff --git a/episodes/read-data.Rmd b/episodes/read-data.Rmd index 426f5df2..23449314 100644 --- a/episodes/read-data.Rmd +++ b/episodes/read-data.Rmd @@ -1,5 +1,5 @@ --- -title: 'read-data' +title: 'Read case data' teaching: 10 exercises: 2 --- @@ -21,93 +21,17 @@ exercises: 2 ## Introduction -This is a lesson created via The Carpentries Workbench. It is written in -[Pandoc-flavored Markdown][pandoc] for static files (with extension `.md`) and -[R Markdown][r-markdown] for dynamic files that can render code into output -(with extension `.Rmd`). Please refer to the [Introduction to The Carpentries -Workbench][carpentries-workbench] for full documentation. +This is a lesson created via The Carpentries Workbench. -What you need to know is that there are three sections required for a valid -Carpentries lesson template: +# Section - 1. `questions` are displayed at the beginning of the episode to prime the - learner for the content. - 2. `objectives` are the learning objectives for an episode displayed with - the questions. - 3. `keypoints` are displayed at the end of the episode to reinforce the - objectives. - -:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: instructor - -Inline instructor notes can help inform instructors of timing challenges -associated with the lessons. They appear in the "Instructor View" - -:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: - -::::::::::::::::::::::::::::::::::::: challenge - -## Challenge 1: Can you do it? - -What is the output of this command? - -```r -paste("This", "new", "lesson", "looks", "good") -``` - -:::::::::::::::::::::::: solution - -## Output - -```output -[1] "This new lesson looks good" -``` - -::::::::::::::::::::::::::::::::: - - -## Challenge 2: how do you nest solutions within challenge blocks? - -:::::::::::::::::::::::: solution - -You can add a line with at least three colons and a `solution` tag. - -::::::::::::::::::::::::::::::::: -:::::::::::::::::::::::::::::::::::::::::::::::: - -## Figures - -You can include figures generated from R Markdown: - -```{r pyramid, fig.alt = "pie chart illusion of a pyramid", fig.cap = "Sun arise each and every morning"} -pie( - c(Sky = 78, "Sunny side of pyramid" = 17, "Shady side of pyramid" = 5), - init.angle = 315, - col = c("deepskyblue", "yellow", "yellow3"), - border = FALSE -) -``` -Or you can use pandoc markdown for static figures with the following syntax: - -`![optional caption that appears below the figure](figure url){alt='alt text for -accessibility purposes'}` - -![You belong in The Carpentries!](https://raw.githubusercontent.com/carpentries/logo/master/Badge_Carpentries.svg){alt='Blue Carpentries hex person logo with no text.'} - -## Math - -One of our episodes contains $\LaTeX$ equations when describing how to create -dynamic reports with {knitr}, so we now use mathjax to describe this: - -`$\alpha = \dfrac{1}{(1 - \beta)^2}$` becomes: $\alpha = \dfrac{1}{(1 - \beta)^2}$ - -Cool, right? +Bla ::::::::::::::::::::::::::::::::::::: keypoints -- Use `.md` files for episodes when you want static content -- Use `.Rmd` files for episodes when you need to generate output -- Run `sandpaper::check_lesson()` to identify any issues with your lesson -- Run `sandpaper::build_lesson()` to preview your lesson locally +- Use `{readepi}` to read data using a consistent and unified interface. +- Use `{cleanepi}` to apply multiple cleaning steps in one function. +- Use `{linelist}` to validate and safeguard your data early in the pipeline. :::::::::::::::::::::::::::::::::::::::::::::::: From 73413597d1d1bfba35a5d98b17bca01ee43972da Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 23 Nov 2023 19:01:52 +0000 Subject: [PATCH 016/130] arrange text in what and how sections --- episodes/fig/Screenshot 2023-11-23 173911.png | Bin 0 -> 590622 bytes episodes/introduction.Rmd | 31 ++++++++++-------- 2 files changed, 17 insertions(+), 14 deletions(-) create mode 100644 episodes/fig/Screenshot 2023-11-23 173911.png diff --git a/episodes/fig/Screenshot 2023-11-23 173911.png b/episodes/fig/Screenshot 2023-11-23 173911.png new file mode 100644 index 0000000000000000000000000000000000000000..c5515d5779ff5c094e0c95768f26451465fd3d8c GIT binary patch literal 590622 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zsCAuC@a!XD=RWNqwQ%8ejb~qtME&FUdWievAH#1ZO;-A}D@vqU`iACT($@R6i`Rrj z2I-$YLwTXDByY&nq{(<0CNuVJ{h*Hcq+IH=ucviD0RCQ|mKr#j|M1EXnV_HPZxWDt zY%6?~N0^{t?1bfEiz0OfP%9KBD}*(iYO*6vT>LA=nTz*Nb=--FVPOM9yzqt}6{nC#G?mo%fH9jB@o6ycG40$^9wGk(2fk6={T@^bN`&Fj}c8HVU|dqF`}zcTEm ziY(vM?KmB(d;W;MYzejK*RoAMRaJW8`R~slLIBf!mhS-6H~xL`Ek&zbqIjdbtcPTPnFkS15fLa%=v}>^>hQ? zfxth)rOE!BKh#a^(x{dpysFCbnPC%j0~DpauY}NdlBak?&F_XUfF87pX>kVp0VT6B zA+O0t@fv+KUueBc7z3y&J$WE^jq#sIplMgVCm{SMli`Bei2wgW-i!ac0_iA~{)z8P zuRpPI|74$Jy%hi7{Rk&HkYV1x2Qn5LAbGL=Emu-1f5ry-w>&OR`}zNVfrmMiNBsU+ VL^}y= Date: Thu, 23 Nov 2023 19:02:20 +0000 Subject: [PATCH 017/130] clear template content --- episodes/describe-data.Rmd | 83 ++------------------------------------ 1 file changed, 4 insertions(+), 79 deletions(-) diff --git a/episodes/describe-data.Rmd b/episodes/describe-data.Rmd index 53a7dc27..72deac12 100644 --- a/episodes/describe-data.Rmd +++ b/episodes/describe-data.Rmd @@ -1,5 +1,5 @@ --- -title: 'describe-data' +title: 'Describe case data' teaching: 10 exercises: 2 --- @@ -26,86 +26,11 @@ exercises: 2 ## Introduction -This is a lesson created via The Carpentries Workbench. It is written in -[Pandoc-flavored Markdown][pandoc] for static files (with extension `.md`) and -[R Markdown][r-markdown] for dynamic files that can render code into output -(with extension `.Rmd`). Please refer to the [Introduction to The Carpentries -Workbench][carpentries-workbench] for full documentation. +This is a lesson created via The Carpentries Workbench. -What you need to know is that there are three sections required for a valid -Carpentries lesson template: +# Content - 1. `questions` are displayed at the beginning of the episode to prime the - learner for the content. - 2. `objectives` are the learning objectives for an episode displayed with - the questions. - 3. `keypoints` are displayed at the end of the episode to reinforce the - objectives. - -:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: instructor - -Inline instructor notes can help inform instructors of timing challenges -associated with the lessons. They appear in the "Instructor View" - -:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: - -::::::::::::::::::::::::::::::::::::: challenge - -## Challenge 1: Can you do it? - -What is the output of this command? - -```r -paste("This", "new", "lesson", "looks", "good") -``` - -:::::::::::::::::::::::: solution - -## Output - -```output -[1] "This new lesson looks good" -``` - -::::::::::::::::::::::::::::::::: - - -## Challenge 2: how do you nest solutions within challenge blocks? - -:::::::::::::::::::::::: solution - -You can add a line with at least three colons and a `solution` tag. - -::::::::::::::::::::::::::::::::: -:::::::::::::::::::::::::::::::::::::::::::::::: - -## Figures - -You can include figures generated from R Markdown: - -```{r pyramid, fig.alt = "pie chart illusion of a pyramid", fig.cap = "Sun arise each and every morning"} -pie( - c(Sky = 78, "Sunny side of pyramid" = 17, "Shady side of pyramid" = 5), - init.angle = 315, - col = c("deepskyblue", "yellow", "yellow3"), - border = FALSE -) -``` -Or you can use pandoc markdown for static figures with the following syntax: - -`![optional caption that appears below the figure](figure url){alt='alt text for -accessibility purposes'}` - -![You belong in The Carpentries!](https://raw.githubusercontent.com/carpentries/logo/master/Badge_Carpentries.svg){alt='Blue Carpentries hex person logo with no text.'} - -## Math - -One of our episodes contains $\LaTeX$ equations when describing how to create -dynamic reports with {knitr}, so we now use mathjax to describe this: - -`$\alpha = \dfrac{1}{(1 - \beta)^2}$` becomes: $\alpha = \dfrac{1}{(1 - \beta)^2}$ - -Cool, right? +Bla ::::::::::::::::::::::::::::::::::::: keypoints From e64dd801a4c0834872efb89e1bef51206cbbf88a Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 24 Nov 2023 11:08:58 +0000 Subject: [PATCH 018/130] rename figure --- ...shot 2023-11-23 173911.png => pkgs-hexlogos.png} | Bin 1 file changed, 0 insertions(+), 0 deletions(-) rename episodes/fig/{Screenshot 2023-11-23 173911.png => pkgs-hexlogos.png} (100%) diff --git a/episodes/fig/Screenshot 2023-11-23 173911.png b/episodes/fig/pkgs-hexlogos.png similarity index 100% rename from episodes/fig/Screenshot 2023-11-23 173911.png rename to episodes/fig/pkgs-hexlogos.png From bdc9edc5d4a77edad6e0743ad4e99b5403c25135 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 24 Nov 2023 11:14:41 +0000 Subject: [PATCH 019/130] remove space within epinow function --- episodes/introduction.Rmd | 1 - 1 file changed, 1 deletion(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 0c82da39..623cb19e 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -89,7 +89,6 @@ generation_time <- dist_spec( ```{r,message=FALSE,warning=FALSE,cache=TRUE} epinow_estimates <- epinow( - # cases reported_cases = example_confirmed[1:60], # delays From 0ccf851dbede2b27750191ef8eca2187a5485033 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 24 Nov 2023 11:30:24 +0000 Subject: [PATCH 020/130] fix intro line to et1 --- episodes/introduction.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 623cb19e..ba75dd9b 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -156,7 +156,7 @@ Epiverse-TRACE aim is to provide a software ecosystem for outbreak analytics. Th Our plan for these tutorials is to introduce key solutions from packages in the outbreak analytics pipeline, plus the required theory concepts to interpret modelling outputs and make rigorous conclusions. -- In the first set of episodes, we are going to learn how to read case data and epidemiological parameters to the _Quantify transmission_ pipeline. These preliminary task are the __Early tasks__. Packages here are `{readepi}`, `{cleanepi}`, `{linelist}`, `{epiparameter}`, and `{episoap}`. +- In the first set of episodes, you will learn how to read epidemiological parameters from systematic reviews directly and standardize your reading and cleaning process to input them into the _Quantify transmission_ pipeline. These preliminary tasks are the __Early tasks__. Packages here are `{readepi}`, `{cleanepi}`, `{linelist}`, `{epiparameter}`, and `{episoap}`. - Then, we are going to get deeper into the packages and required theory to _Quantify transmission_ and perform more real-time analysis task next to it. These are the __Middle tasks__. Packages here are `{EpiNow2}`, `{cfr}`, `{epichains}`, and `{superspreading}`. From d74e4dcb21372fb14f12d989ae28fc4858c56206 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 24 Nov 2023 11:51:38 +0000 Subject: [PATCH 021/130] rewrite intro sentence --- episodes/introduction.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index ba75dd9b..4ae1f2c5 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -156,7 +156,7 @@ Epiverse-TRACE aim is to provide a software ecosystem for outbreak analytics. Th Our plan for these tutorials is to introduce key solutions from packages in the outbreak analytics pipeline, plus the required theory concepts to interpret modelling outputs and make rigorous conclusions. -- In the first set of episodes, you will learn how to read epidemiological parameters from systematic reviews directly and standardize your reading and cleaning process to input them into the _Quantify transmission_ pipeline. These preliminary tasks are the __Early tasks__. Packages here are `{readepi}`, `{cleanepi}`, `{linelist}`, `{epiparameter}`, and `{episoap}`. +- In the first set of episodes, you will learn how to optimize the reading and cleaning of epidemiological parameters and case data to input them into the _Quantify transmission_ pipeline. These preliminary tasks are the __Early tasks__. Packages here are `{readepi}`, `{cleanepi}`, `{linelist}`, `{epiparameter}`, and `{episoap}`. - Then, we are going to get deeper into the packages and required theory to _Quantify transmission_ and perform more real-time analysis task next to it. These are the __Middle tasks__. Packages here are `{EpiNow2}`, `{cfr}`, `{epichains}`, and `{superspreading}`. From a92d6d4f63247206a94b1c6dcdb697334ec414e0 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 24 Nov 2023 11:59:00 +0000 Subject: [PATCH 022/130] add prerequisites --- episodes/introduction.Rmd | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 4ae1f2c5..1fa6129a 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -26,7 +26,13 @@ editor_options: ## Prerequisites -List (and hyperlink) the lessons/packages which need to be covered before this lesson +This episode requires you to be familiar with: + +**Data science** : Basic programming with R. + +**Epidemic theory** : Effective reproduction number. + +This also requires the following package: ```r install_version( From c8cc532d284f25f9aa0a9024121955b658b5462f Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 24 Nov 2023 21:09:00 +0000 Subject: [PATCH 023/130] replace prerequisite --- episodes/introduction.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 1fa6129a..6822b9a1 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -30,7 +30,7 @@ This episode requires you to be familiar with: **Data science** : Basic programming with R. -**Epidemic theory** : Effective reproduction number. +**Epidemic theory** : Reproduction number. This also requires the following package: From 8a343abda9d3f9690cf89cd678e763b24e5bdc48 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 24 Nov 2023 21:11:21 +0000 Subject: [PATCH 024/130] add clear wording --- episodes/introduction.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 6822b9a1..2e44bf4b 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -164,7 +164,7 @@ Our plan for these tutorials is to introduce key solutions from packages in the - In the first set of episodes, you will learn how to optimize the reading and cleaning of epidemiological parameters and case data to input them into the _Quantify transmission_ pipeline. These preliminary tasks are the __Early tasks__. Packages here are `{readepi}`, `{cleanepi}`, `{linelist}`, `{epiparameter}`, and `{episoap}`. -- Then, we are going to get deeper into the packages and required theory to _Quantify transmission_ and perform more real-time analysis task next to it. These are the __Middle tasks__. Packages here are `{EpiNow2}`, `{cfr}`, `{epichains}`, and `{superspreading}`. +- Then, we will get deeper into the packages and required theory to _Quantify transmission_ and perform more real-time analysis tasks next to it. These are the __Middle tasks__. Packages here are `{EpiNow2}`, `{cfr}`, `{epichains}`, and `{superspreading}`. - Lastly, we will use _Quantify transmission_ data outputs to compare it to other indicators and simulate epidemic scenarios as part of the __Late tasks__. Packages here are `{finalsize}`, `{epidemics}`, `{scenarios}`, and `{vaccineff}`. From bd24bf5275fd951d883a033a91a32bb57d3627f8 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 24 Nov 2023 21:11:44 +0000 Subject: [PATCH 025/130] add text on why and epinow2 steps --- episodes/introduction.Rmd | 73 ++++++++++++++++++++++----------------- 1 file changed, 41 insertions(+), 32 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 2e44bf4b..43d3e981 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -43,35 +43,54 @@ install_version( ::::::::::::::::::::::::::::::::: +::::::::::::::::::::::::::::::::: discussion + +### Questions to think about + +Remember your last experience with outbreak data and reflect in these questions: + +- What data sources you needed to understand the outbreak? +- How did you get accessed to that data? +- Is that analysis pipeline you followed reusable for a next response? -## Introduction +Pair up and share your experiences. + +::::::::::::::::::::::::::::::::::::::::::: + + +## Why to use R packages for Outbreak analytics? -_write about the Reproduction number (in a motivational way)_ +Outbreaks appear with different diseases and in different contexts but what all of them have in common are the key public health questions ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371#d1e605)). -Packages are handy tools to reuse code, maintenance, less error prone data analysis steps. +Is the epidemic going to take off? Is it under control? How much effort will be needed to control it? We can answer to them by _quantifying the transmissibility_ of the disease. The most used parameter for this is the reproduction number ($R$), the average number of secondary infections caused by a typical primary case in the population +of interest([Prism, 2016](http://prism.edu.au/publications/prism-modeling-guideline/)). We can intuitively interpret it as: -## Quantify transmission +- if $R>1$, the epidemic is likely to growth, +- if $R<1$, the epidemic is likely to decline. -The `{EpiNow2}` provide us with a a three-steps solution for this task! +To estimate the reproduction number we need to collect __data inputs__ like the incidence of reported cases and the [serial interval](../learners/reference.md#serialinterval) distribution. We need to apply the appropriate mathematical models written in code with the required computational methods to get them. That is not enough! Following _good practices_, that code should be peer-reviewed and contain internal tests to double-check that we are getting the estimates we expect. Imagine rewriting all of it during a health emergency! + +In R, the fundamental unit of shareable code is the _package_. A package bundles together code, data, documentation, and tests, and is easy to share with others ([Wickham and Bryan, 2023](https://r-pkgs.org/introduction.html)). We, as epidemiologists, can contribute to their collaborative maintenance as a community of practice, helping us to perform less error-prone data analysis pipelines. + +## Example: Quantify transmission + +The `{EpiNow2}` package provide us with a three-steps solution to _quantify the transmissibility_. ```{r,warning=FALSE} library(EpiNow2) ``` +### First, get your case data -### First, get your data - -A data frame of observation +Case incidence data needs be stored in a data frame with the observed number of cases per day: ```{r} example_confirmed ``` -### Then, set the parameters +### Then, set the delay distributions -The natural history of a disease influence the transmission - -delay distributions +The natural history of a disease influence the transmission dynamics. For `{EpiNow2}`, we can specify relevant delay distributions like the [incubation period](../learners/reference.md#incubation), the [reporting delay](../learners/reference.md#reportingdelay), and [generation time](../learners/reference.md#generationtime): ```{r} incubation_period <- dist_spec( @@ -91,7 +110,9 @@ generation_time <- dist_spec( ) ``` -### Let's calculate the reproductive number! +### Let's calculate the reproduction number! + +In the `epinow()` function we can add the cases, delays, and your preferred computational parameters for this calculation: ```{r,message=FALSE,warning=FALSE,cache=TRUE} epinow_estimates <- epinow( @@ -110,25 +131,27 @@ epinow_estimates <- epinow( ) ``` +Getting the following plot that includes cases by date of report, by date of infection, and with the time-varying [effective](https://wwwnc.cdc.gov/eid/article/29/8/22-1445_article) reproduction number: + ```{r} base::plot(epinow_estimates) ``` ## The problem! -However, doing this in real life is not as easy as this example! +However, _quantify the transmissibility_ during an outbreak response is not as easy as this example! -Usually, we receive outbreak data in non-standard formats that require specific steps for make them useful data inputs. Some of them are: +Usually, we receive outbreak data in non-standard formats, requiring specific steps and taking the most time to prepare usable data inputs. Some of them are: - Read parameters from literature - Read and clean case data - Validate your linelist - Describe case data -After this, we can use the reproductive number and other outputs as inputs for other outbreak analytics tasks. For example: +And this is not the end. After _quantifying transmissibility_ we need to answer more questions like: What is the attack rate we expect? What would be the impact of a given intervention? We can use the reproduction number and other outputs as new inputs for complementary tasks. For example: -- Forecast cases - Estimate severity +- Create short-term forecast - Simulate transmission scenarios - Compare interventions @@ -136,23 +159,9 @@ So, all these tasks can be interconnected in a pipeline: ![The outbreak analytics pipeline.](https://epiverse-trace.github.io/task_pipeline-minimal.svg) -::::::::::::::::::::::::::::::::: discussion - -### Questions to think about - -Remember your last experience with outbreak data and reflect in these questions: - -- What data sources you needed to understand the outbreak? -- How did you get accessed to that data? -- Is that analysis pipeline you followed reusable for a next response? - -Pair up and share your experiences. - -::::::::::::::::::::::::::::::::::::::::::: - ## What can we do? -One strategy is to incorporate specialized R packages into your traditional analysis pipeline. These packages should fill the gaps of these epidemiology specific challenges in response to outbreaks. +Our strategy is to gradually incorporate specialized R packages into your traditional analysis pipeline. These packages should fill the gaps of these epidemiology specific tasks in response to outbreaks. Epiverse-TRACE aim is to provide a software ecosystem for outbreak analytics. This includes to support the development of software pieces, make the existing ones interoperable for the user experience, and stimulate a community of practice. From d6175dc1522ee6cd5a96ebc74f416f146ee3c8a5 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 24 Nov 2023 21:12:19 +0000 Subject: [PATCH 026/130] add definitions for delays --- learners/reference.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/learners/reference.md b/learners/reference.md index 1b73c9d5..075c7040 100644 --- a/learners/reference.md +++ b/learners/reference.md @@ -37,6 +37,9 @@ title: 'Glossary of Terms: Epiverse-TRACE' ## G +[Generation time]{#generationtime} +: Distribution of time between infection of an index case and infection of its secondary cases. Most commonly, the generation time distribution is estimated from data on the [serial interval](#serialinterval) distribution of an infection ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). + [Growth rate]{#growth} : The exponential growth rate tells us how much cases are increasing or decreasing at the start of an epidemic. It gives us a measure of speed of transmission, see [Dushoff & Park, 2021](https://royalsocietypublishing.org/doi/full/10.1098/rspb.2020.1556). @@ -80,13 +83,19 @@ title: 'Glossary of Terms: Epiverse-TRACE' - +## R + +[Reporting delay]{#reportingdelay} +: Delay between the time an event occurs and the time it is reported ([Lawless, 1994](https://www.jstor.org/stable/3315820)). We can quantify it by comparing the linelist with successive versions of it or up-to-date reported aggregated case counts ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). ## S [State variables]{#state} : The state variables in a model represented by [ordinary differential equations](#ordinary) are the disease states that individuals can be in e.g. if individuals can be susceptible, infectious or recovered the state variables are $S$, $I$ and $R$. There is an ordinary differential equation for each state variable. +[Serial interval]{#serialinterval} +: The time delay between the onset of symptoms between a primary case and a secondary case. The serial interval is related to the [incubation period](#incubation), as described in Figure 2 at [Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext). + [Stochastic model]{#stochastic} : A model that includes some stochastic process resulting in variation in model simulations for the same initial conditions and parameter values. Examples include stochastic differential equations and branching process models. For more detail see [Allen (2017)](https://doi.org/10.1016/j.idm.2017.03.001). From 545e8e7e4e4c7feda3b3c849fd2e56ccb318a658 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 24 Nov 2023 21:19:38 +0000 Subject: [PATCH 027/130] replace link with new R0 definition --- episodes/introduction.Rmd | 2 +- learners/reference.md | 6 +++++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 43d3e981..9a3e8a68 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -131,7 +131,7 @@ epinow_estimates <- epinow( ) ``` -Getting the following plot that includes cases by date of report, by date of infection, and with the time-varying [effective](https://wwwnc.cdc.gov/eid/article/29/8/22-1445_article) reproduction number: +Getting the following plot that includes cases by date of report, by date of infection, and with the time-varying (or [effective](../learners/reference.md#effectiverepro)) reproduction number: ```{r} base::plot(epinow_estimates) diff --git a/learners/reference.md b/learners/reference.md index 075c7040..d6ae325c 100644 --- a/learners/reference.md +++ b/learners/reference.md @@ -32,7 +32,11 @@ title: 'Glossary of Terms: Epiverse-TRACE' [Direct transmission]{#direct} : Individuals become infected via direct contact with other infected humans. Airborne transmitted infections are often modelled as directly transmitted infections as they require close contact with infected individuals for successful transmission. - +## E + +[Effective reproduction number]{#effectiverepro} +: The effective reproduction number ($Rt$) is similar to the [Basic reproductive number](#basic) ($R0$), but $Rt$ measures the number of persons infected by infectious person when some portion of the population has already been infected. Read more about the [etymology of Reproduction number by Sharma et al, 2023](https://wwwnc.cdc.gov/eid/article/29/8/22-1445_article). + From ad7f23e4ba96950252b5952da8108226afa9b2d6 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 24 Nov 2023 21:21:38 +0000 Subject: [PATCH 028/130] specify the task as a point of reference --- episodes/introduction.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 9a3e8a68..0bf57fb2 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -169,9 +169,9 @@ Epiverse-TRACE aim is to provide a software ecosystem for outbreak analytics. Th ## How can I start? -Our plan for these tutorials is to introduce key solutions from packages in the outbreak analytics pipeline, plus the required theory concepts to interpret modelling outputs and make rigorous conclusions. +Our plan for these tutorials is to introduce key solutions from packages in the tasks before and after the _Quantify transmission_ task, plus the required theory concepts to interpret modelling outputs and make rigorous conclusions. -- In the first set of episodes, you will learn how to optimize the reading and cleaning of epidemiological parameters and case data to input them into the _Quantify transmission_ pipeline. These preliminary tasks are the __Early tasks__. Packages here are `{readepi}`, `{cleanepi}`, `{linelist}`, `{epiparameter}`, and `{episoap}`. +- In the first set of episodes, you will learn how to optimize the reading and cleaning of epidemiological parameters and case data to input them into the _Quantify transmission_ task. These preliminary tasks are the __Early tasks__. Packages here are `{readepi}`, `{cleanepi}`, `{linelist}`, `{epiparameter}`, and `{episoap}`. - Then, we will get deeper into the packages and required theory to _Quantify transmission_ and perform more real-time analysis tasks next to it. These are the __Middle tasks__. Packages here are `{EpiNow2}`, `{cfr}`, `{epichains}`, and `{superspreading}`. From fbd0eda9d8027e7370a8a75373cc7177be4df3a5 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 24 Nov 2023 21:52:13 +0000 Subject: [PATCH 029/130] edits after first review with grammarly --- episodes/introduction.Rmd | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 0bf57fb2..a252778d 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -63,14 +63,14 @@ Pair up and share your experiences. Outbreaks appear with different diseases and in different contexts but what all of them have in common are the key public health questions ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371#d1e605)). Is the epidemic going to take off? Is it under control? How much effort will be needed to control it? We can answer to them by _quantifying the transmissibility_ of the disease. The most used parameter for this is the reproduction number ($R$), the average number of secondary infections caused by a typical primary case in the population -of interest([Prism, 2016](http://prism.edu.au/publications/prism-modeling-guideline/)). We can intuitively interpret it as: +of interest ([Prism, 2016](http://prism.edu.au/publications/prism-modeling-guideline/)). We can intuitively interpret it as: - if $R>1$, the epidemic is likely to growth, - if $R<1$, the epidemic is likely to decline. -To estimate the reproduction number we need to collect __data inputs__ like the incidence of reported cases and the [serial interval](../learners/reference.md#serialinterval) distribution. We need to apply the appropriate mathematical models written in code with the required computational methods to get them. That is not enough! Following _good practices_, that code should be peer-reviewed and contain internal tests to double-check that we are getting the estimates we expect. Imagine rewriting all of it during a health emergency! +To estimate the reproduction number we need to collect __data inputs__ like the incidence of reported cases and the [serial interval](../learners/reference.md#serialinterval) distribution. Then, to get it, we need to apply the appropriate mathematical models written in code with the required computational methods. That is not enough! Following _good practices_, that code should be peer-reviewed and contain internal tests to double-check that we are getting the estimates we expect. Imagine rewriting all of it during a health emergency! -In R, the fundamental unit of shareable code is the _package_. A package bundles together code, data, documentation, and tests, and is easy to share with others ([Wickham and Bryan, 2023](https://r-pkgs.org/introduction.html)). We, as epidemiologists, can contribute to their collaborative maintenance as a community of practice, helping us to perform less error-prone data analysis pipelines. +In R, the fundamental unit of shareable code is the _package_. A package bundles together code, data, documentation, and tests, and is easy to share with others ([Wickham and Bryan, 2023](https://r-pkgs.org/introduction.html)). We, as epidemiologists, can contribute to their collaborative maintenance as a community, to perform less error-prone data analysis pipelines. ## Example: Quantify transmission @@ -90,7 +90,7 @@ example_confirmed ### Then, set the delay distributions -The natural history of a disease influence the transmission dynamics. For `{EpiNow2}`, we can specify relevant delay distributions like the [incubation period](../learners/reference.md#incubation), the [reporting delay](../learners/reference.md#reportingdelay), and [generation time](../learners/reference.md#generationtime): +The natural history of a disease influence the transmission dynamics. For `{EpiNow2}`, we can specify relevant delay distributions like the [incubation period](../learners/reference.md#incubation), the [reporting delay](../learners/reference.md#reportingdelay), and the [generation time](../learners/reference.md#generationtime): ```{r} incubation_period <- dist_spec( @@ -125,13 +125,13 @@ epinow_estimates <- epinow( ), # computation stan = stan_opts( - cores = 4, samples = 1000, chains = 2, + cores = 4, samples = 1000, chains = 3, control = list(adapt_delta = 0.99) ) ) ``` -Getting the following plot that includes cases by date of report, by date of infection, and with the time-varying (or [effective](../learners/reference.md#effectiverepro)) reproduction number: +Getting the time-varying (or [effective](../learners/reference.md#effectiverepro)) reproduction number next to the cases by date of report and cases by date of infection: ```{r} base::plot(epinow_estimates) @@ -139,7 +139,7 @@ base::plot(epinow_estimates) ## The problem! -However, _quantify the transmissibility_ during an outbreak response is not as easy as this example! +However, _quantifying the transmissibility_ during a real-life outbreak response is more challenging than this example! Usually, we receive outbreak data in non-standard formats, requiring specific steps and taking the most time to prepare usable data inputs. Some of them are: @@ -148,7 +148,7 @@ Usually, we receive outbreak data in non-standard formats, requiring specific st - Validate your linelist - Describe case data -And this is not the end. After _quantifying transmissibility_ we need to answer more questions like: What is the attack rate we expect? What would be the impact of a given intervention? We can use the reproduction number and other outputs as new inputs for complementary tasks. For example: +And this is not the end. After _quantifying transmissibility_ we need to answer more key public health questions like: What is the attack rate we expect? What would be the impact of a given intervention? We can use the reproduction number and other outputs as new inputs for complementary tasks. For example: - Estimate severity - Create short-term forecast @@ -161,15 +161,15 @@ So, all these tasks can be interconnected in a pipeline: ## What can we do? -Our strategy is to gradually incorporate specialized R packages into your traditional analysis pipeline. These packages should fill the gaps of these epidemiology specific tasks in response to outbreaks. +Our strategy is to gradually incorporate specialized R packages into the traditional analysis pipeline. These packages should fill the gaps in these epidemiology-specific tasks in response to outbreaks. -Epiverse-TRACE aim is to provide a software ecosystem for outbreak analytics. This includes to support the development of software pieces, make the existing ones interoperable for the user experience, and stimulate a community of practice. +Epiverse-TRACE aim is to provide a software ecosystem for outbreak analytics. We support the development of software pieces, make the existing ones interoperable for the user experience, and stimulate a community of practice. ![](fig/pkgs-hexlogos.png) ## How can I start? -Our plan for these tutorials is to introduce key solutions from packages in the tasks before and after the _Quantify transmission_ task, plus the required theory concepts to interpret modelling outputs and make rigorous conclusions. +Our plan for these tutorials is to introduce key solutions from packages in all the tasks before and after the _Quantify transmission_ task, plus the required theory concepts to interpret modelling outputs and make rigorous conclusions. - In the first set of episodes, you will learn how to optimize the reading and cleaning of epidemiological parameters and case data to input them into the _Quantify transmission_ task. These preliminary tasks are the __Early tasks__. Packages here are `{readepi}`, `{cleanepi}`, `{linelist}`, `{epiparameter}`, and `{episoap}`. From a6659c9e559dc28e9515505eb3e023f8555119e0 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 24 Nov 2023 21:55:44 +0000 Subject: [PATCH 030/130] relocate discussion after why section --- episodes/introduction.Rmd | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index a252778d..c4759e44 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -43,6 +43,20 @@ install_version( ::::::::::::::::::::::::::::::::: +## Why to use R packages for Outbreak analytics? + +Outbreaks appear with different diseases and in different contexts but what all of them have in common are the key public health questions ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371#d1e605)). + +Is the epidemic going to take off? Is it under control? How much effort will be needed to control it? We can answer to them by _quantifying the transmissibility_ of the disease. The most used parameter for this is the reproduction number ($R$), the average number of secondary infections caused by a typical primary case in the population +of interest ([Prism, 2016](http://prism.edu.au/publications/prism-modeling-guideline/)). We can intuitively interpret it as: + +- if $R>1$, the epidemic is likely to growth, +- if $R<1$, the epidemic is likely to decline. + +To estimate the reproduction number we need to collect __data inputs__ like the incidence of reported cases and the [serial interval](../learners/reference.md#serialinterval) distribution. Then, to get it, we need to apply the appropriate mathematical models written in code with the required computational methods. That is not enough! Following _good practices_, that code should be peer-reviewed and contain internal tests to double-check that we are getting the estimates we expect. Imagine rewriting all of it during a health emergency! + +In R, the fundamental unit of shareable code is the _package_. A package bundles together code, data, documentation, and tests, and is easy to share with others ([Wickham and Bryan, 2023](https://r-pkgs.org/introduction.html)). We, as epidemiologists, can contribute to their collaborative maintenance as a community, to perform less error-prone data analysis pipelines. + ::::::::::::::::::::::::::::::::: discussion ### Questions to think about @@ -58,20 +72,6 @@ Pair up and share your experiences. ::::::::::::::::::::::::::::::::::::::::::: -## Why to use R packages for Outbreak analytics? - -Outbreaks appear with different diseases and in different contexts but what all of them have in common are the key public health questions ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371#d1e605)). - -Is the epidemic going to take off? Is it under control? How much effort will be needed to control it? We can answer to them by _quantifying the transmissibility_ of the disease. The most used parameter for this is the reproduction number ($R$), the average number of secondary infections caused by a typical primary case in the population -of interest ([Prism, 2016](http://prism.edu.au/publications/prism-modeling-guideline/)). We can intuitively interpret it as: - -- if $R>1$, the epidemic is likely to growth, -- if $R<1$, the epidemic is likely to decline. - -To estimate the reproduction number we need to collect __data inputs__ like the incidence of reported cases and the [serial interval](../learners/reference.md#serialinterval) distribution. Then, to get it, we need to apply the appropriate mathematical models written in code with the required computational methods. That is not enough! Following _good practices_, that code should be peer-reviewed and contain internal tests to double-check that we are getting the estimates we expect. Imagine rewriting all of it during a health emergency! - -In R, the fundamental unit of shareable code is the _package_. A package bundles together code, data, documentation, and tests, and is easy to share with others ([Wickham and Bryan, 2023](https://r-pkgs.org/introduction.html)). We, as epidemiologists, can contribute to their collaborative maintenance as a community, to perform less error-prone data analysis pipelines. - ## Example: Quantify transmission The `{EpiNow2}` package provide us with a three-steps solution to _quantify the transmissibility_. From 57b2bca0c7cb8da3bb57ab05d5b93ff71ee3a8b3 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Wed, 29 Nov 2023 17:38:40 +0000 Subject: [PATCH 031/130] rename episode for delays previous to read data --- config.yaml | 9 +++++---- episodes/{describe-data.Rmd => read-delays.Rmd} | 0 2 files changed, 5 insertions(+), 4 deletions(-) rename episodes/{describe-data.Rmd => read-delays.Rmd} (100%) diff --git a/config.yaml b/config.yaml index 93ee5c98..59be1721 100644 --- a/config.yaml +++ b/config.yaml @@ -58,8 +58,9 @@ contact: 'andree.valle-campos@lshtm.ac.uk' #FIXME # - another-learner.md # Order of episodes in your lesson -episodes: +episodes: - introduction.Rmd +- read-delays.Rmd - read-data.Rmd - describe-data.Rmd - quantify-transmissibility.Rmd @@ -70,13 +71,13 @@ episodes: - compare-interventions.Rmd # Information for Learners -learners: +learners: # Information for Instructors -instructors: +instructors: # Learner Profiles -profiles: +profiles: # Customisation --------------------------------------------- # diff --git a/episodes/describe-data.Rmd b/episodes/read-delays.Rmd similarity index 100% rename from episodes/describe-data.Rmd rename to episodes/read-delays.Rmd From 1a32ca8cc848f3e097cbaa005185b7dac03c53f8 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Wed, 29 Nov 2023 18:00:13 +0000 Subject: [PATCH 032/130] add placeholder for episoap episode --- config.yaml | 20 ++++++++++---------- episodes/create-report.Rmd | 28 ++++++++++++++++++++++++++++ 2 files changed, 38 insertions(+), 10 deletions(-) create mode 100644 episodes/create-report.Rmd diff --git a/config.yaml b/config.yaml index 59be1721..78ae2dc3 100644 --- a/config.yaml +++ b/config.yaml @@ -14,17 +14,17 @@ carpentry: 'incubator' title: 'Outbreak analytics with R' # Date the lesson was created (YYYY-MM-DD, this is empty by default) -created: ~ +created: # Comma-separated list of keywords for the lesson -keywords: 'forecasts, epidemic models, interventions' #FIXME +keywords: 'forecasts, epidemic models, interventions' # Life cycle stage of the lesson # possible values: pre-alpha, alpha, beta, stable life_cycle: 'pre-alpha' -# License of the lesson -license: 'CC-BY 4.0' #FIXME +# License of the lesson materials (recommended CC-BY 4.0) +license: 'CC-BY 4.0' # Link to the source repository for this lesson source: 'https://github.com/epiverse-trace/tutorials' @@ -33,7 +33,7 @@ source: 'https://github.com/epiverse-trace/tutorials' branch: 'main' # Who to contact if there are any issues -contact: 'andree.valle-campos@lshtm.ac.uk' #FIXME +contact: 'andree.valle-campos@lshtm.ac.uk' # Navigation ------------------------------------------------ # @@ -58,11 +58,12 @@ contact: 'andree.valle-campos@lshtm.ac.uk' #FIXME # - another-learner.md # Order of episodes in your lesson -episodes: +episodes: - introduction.Rmd - read-delays.Rmd - read-data.Rmd - describe-data.Rmd +- create-report.Rmd - quantify-transmissibility.Rmd - create-forecast.Rmd - simulating-transmission.Rmd @@ -71,20 +72,19 @@ episodes: - compare-interventions.Rmd # Information for Learners -learners: +learners: # Information for Instructors -instructors: +instructors: # Learner Profiles -profiles: +profiles: # Customisation --------------------------------------------- # # This space below is where custom yaml items (e.g. pinning # sandpaper and varnish versions) should live - varnish: epiverse-trace/varnish@epiversetheme # this is carpentries/sandpaper#533 in our fork so we can keep it up to date with main sandpaper: epiverse-trace/sandpaper@patch-renv-github-bug diff --git a/episodes/create-report.Rmd b/episodes/create-report.Rmd new file mode 100644 index 00000000..27e34662 --- /dev/null +++ b/episodes/create-report.Rmd @@ -0,0 +1,28 @@ +--- +title: 'create-report' +teaching: 10 +exercises: 2 +--- + +:::::::::::::::::::::::::::::::::::::: questions + +- How to write a reproducible report with `{episoap}`? + +:::::::::::::::::::::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::: objectives + +- Create a reproducible situation report. + +:::::::::::::::::::::::::::::::::::::::::::::::: + +## Introduction + +This is a lesson created via The Carpentries Workbench. + +::::::::::::::::::::::::::::::::::::: keypoints + +- Use `{episoap}` to + +:::::::::::::::::::::::::::::::::::::::::::::::: + From c779f18b501775cbd734238c542085d57bc79b39 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Wed, 29 Nov 2023 20:46:25 +0000 Subject: [PATCH 033/130] rename episode for read cases data --- config.yaml | 12 ++++++------ episodes/{read-data.Rmd => read-cases.Rmd} | 0 2 files changed, 6 insertions(+), 6 deletions(-) rename episodes/{read-data.Rmd => read-cases.Rmd} (100%) diff --git a/config.yaml b/config.yaml index 78ae2dc3..b32f919a 100644 --- a/config.yaml +++ b/config.yaml @@ -14,7 +14,7 @@ carpentry: 'incubator' title: 'Outbreak analytics with R' # Date the lesson was created (YYYY-MM-DD, this is empty by default) -created: +created: # Comma-separated list of keywords for the lesson keywords: 'forecasts, epidemic models, interventions' @@ -58,10 +58,10 @@ contact: 'andree.valle-campos@lshtm.ac.uk' # - another-learner.md # Order of episodes in your lesson -episodes: +episodes: - introduction.Rmd - read-delays.Rmd -- read-data.Rmd +- read-cases.Rmd - describe-data.Rmd - create-report.Rmd - quantify-transmissibility.Rmd @@ -72,13 +72,13 @@ episodes: - compare-interventions.Rmd # Information for Learners -learners: +learners: # Information for Instructors -instructors: +instructors: # Learner Profiles -profiles: +profiles: # Customisation --------------------------------------------- # diff --git a/episodes/read-data.Rmd b/episodes/read-cases.Rmd similarity index 100% rename from episodes/read-data.Rmd rename to episodes/read-cases.Rmd From bba336d34602e154f4354585eb99a6ddb8382ebe Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 30 Nov 2023 03:21:52 +0000 Subject: [PATCH 034/130] use a simpler epinow2 with generation time only --- episodes/introduction.Rmd | 16 +--------------- 1 file changed, 1 insertion(+), 15 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index c4759e44..7c64a28f 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -90,20 +90,9 @@ example_confirmed ### Then, set the delay distributions -The natural history of a disease influence the transmission dynamics. For `{EpiNow2}`, we can specify relevant delay distributions like the [incubation period](../learners/reference.md#incubation), the [reporting delay](../learners/reference.md#reportingdelay), and the [generation time](../learners/reference.md#generationtime): +The natural history of a disease influence the transmission dynamics. For `{EpiNow2}`, we can specify a relevant epidemiological distributions, the [generation time](../learners/reference.md#generationtime): ```{r} -incubation_period <- dist_spec( - mean = 4, sd = 2, - max = 20, distribution = "gamma" -) - -reporting_delay <- dist_spec( - mean = convert_to_logmean(2, 1), - sd = convert_to_logsd(2, 1), - max = 10, distribution = "lognormal" -) - generation_time <- dist_spec( mean = 3.6, sd = 3.1, max = 20, distribution = "lognormal" @@ -120,9 +109,6 @@ epinow_estimates <- epinow( reported_cases = example_confirmed[1:60], # delays generation_time = generation_time_opts(generation_time), - delays = delay_opts( - incubation_period + reporting_delay - ), # computation stan = stan_opts( cores = 4, samples = 1000, chains = 3, From bea61b7f98171b2b8acf1d4b3a42f253c714b8b0 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 30 Nov 2023 03:22:33 +0000 Subject: [PATCH 035/130] add summative code content this connects epiparameter to epinow2 --- episodes/read-delays.Rmd | 195 ++++++++++++++++++++++++++++++++++++++- 1 file changed, 192 insertions(+), 3 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 72deac12..bd1cfaad 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -24,13 +24,202 @@ exercises: 2 :::::::::::::::::::::::::::::::::::::::::::::::: -## Introduction +## Systematic review of Parameters This is a lesson created via The Carpentries Workbench. -# Content +# Find a generation time + +```{r} +### 1 replace generation time from literature ----- + +generation_time <- dist_spec( + mean = 3.6, + sd = 3.1, + max = 20, + distribution = "lognormal" +) + +# replace this assuming that this is covid +# with lognormal + +epiparameter::epidist_db( + disease = "covid", + epi_dist = "serial", + author = "Nishiura") %>% + epiparameter::list_distributions() + +covid_lnorm <- epiparameter::epidist_db( + disease = "covid", + epi_dist = "serial", + author = "Nishiura", + single_epidist = T) # not desired + +covid_lnorm + +covid_lnorm$summary_stats$mean +covid_lnorm_discrete <- epiparameter::discretise(covid_lnorm) +covid_lnorm_discrete_max <- covid_lnorm_discrete$prob_dist$q(p = 0.999) + +generation_time_epiparam <- dist_spec( + mean = covid_lnorm$summary_stats$mean, + sd = covid_lnorm$summary_stats$sd, + max = covid_lnorm_discrete_max, + distribution = "lognormal" +) + +epinow_estimates <- epinow( + # cases + reported_cases = example_confirmed[1:60], + # delays + generation_time = generation_time_opts(generation_time_epiparam), + # computation + stan = stan_opts( + cores = 4, samples = 1000, chains = 3, + control = list(adapt_delta = 0.99) + ) +) + +base::plot(epinow_estimates) +``` + +## formative + +```{r} +# generation time for ebola +# if not, find serial interval and use it as serial interval +``` + + +## Adding incubation time + +The natural history of a disease influence the transmission dynamics. For `{EpiNow2}`, we can specify relevant delay distributions like the [incubation period](../learners/reference.md#incubation): + +```{r} +incubation_period <- dist_spec( + mean = 4, sd = 2, + max = 20, distribution = "gamma" +) +``` + +```{r,message=FALSE,warning=FALSE,cache=TRUE} +epinow_estimates <- epinow( + # cases + reported_cases = example_confirmed[1:60], + # delays + generation_time = generation_time_opts(generation_time), + delays = delay_opts(incubation_period), + # computation + stan = stan_opts( + cores = 4, samples = 1000, chains = 3, + control = list(adapt_delta = 0.99) + ) +) +``` + + +```{r} +### 2 include incubation time ------ + +epiparameter::epidist_db( + disease = "covid", + epi_dist = "incubation") %>% + epiparameter::list_distributions() + +covid_incubation <- epiparameter::epidist_db( + disease = "covid", + epi_dist = "incubation", + author = "Natalie", + single_epidist = T +) + +covid_incubation + +covid_incubation$summary_stats$mean +covid_incubation_discrete <- epiparameter::discretise(covid_incubation) +covid_incubation_discrete_max <- covid_lnorm_discrete$prob_dist$q(p = 0.999) + +incubation_time_epiparam <- dist_spec( + mean = covid_incubation$summary_stats$mean, + sd = covid_incubation$summary_stats$sd, + max = covid_incubation_discrete_max, + distribution = "lognormal" +) + +epinow_estimates <- epinow( + # cases + reported_cases = example_confirmed[1:60], + # generation time + generation_time = generation_time_opts(generation_time_epiparam), + # delays + delays = delay_opts(incubation_time_epiparam), + # computation + stan = stan_opts( + cores = 4, samples = 1000, chains = 3, + control = list(adapt_delta = 0.99) + ) +) + +base::plot(epinow_estimates) + +# post calculation questions +# what have changed? +# - cases by date of infection +# - uncertainty + +``` + +## formative + +```{r} +# formative, now with ebola ----------------------------------------------- + +# read ebola + + +``` + + +## Adding reporting delays + +The natural history of a disease influence the transmission dynamics. For `{EpiNow2}`, we can specify relevant delay distributions like the [reporting delay](../learners/reference.md#reportingdelay): + +```{r} +reporting_delay <- dist_spec( + mean = convert_to_logmean(2, 1), + sd = convert_to_logsd(2, 1), + max = 10, distribution = "lognormal" +) +``` + +```{r,message=FALSE,warning=FALSE,cache=TRUE} +epinow_estimates <- epinow( + # cases + reported_cases = example_confirmed[1:60], + # delays + generation_time = generation_time_opts(generation_time), + delays = delay_opts( + incubation_period + reporting_delay + ), + # computation + stan = stan_opts( + cores = 4, samples = 1000, chains = 3, + control = list(adapt_delta = 0.99) + ) +) +``` + +## formative + +```{r} +### 3 add reporting delay -------- + +# now is your turn! +# after the ebola assessment + + +``` -Bla ::::::::::::::::::::::::::::::::::::: keypoints From 49dd781475bdba42a7560cebe1b4bdfba45af0a8 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 30 Nov 2023 03:27:06 +0000 Subject: [PATCH 036/130] add sections content and names --- episodes/read-delays.Rmd | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index bd1cfaad..903a921d 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -28,7 +28,10 @@ exercises: 2 This is a lesson created via The Carpentries Workbench. -# Find a generation time +## Find a generation time + +What it is? +With which other is related? -> the serial interval ```{r} ### 1 replace generation time from literature ----- @@ -91,7 +94,10 @@ base::plot(epinow_estimates) ``` -## Adding incubation time +## Find the incubation time + +What it is? +With which other is related? The natural history of a disease influence the transmission dynamics. For `{EpiNow2}`, we can specify relevant delay distributions like the [incubation period](../learners/reference.md#incubation): @@ -180,7 +186,10 @@ base::plot(epinow_estimates) ``` -## Adding reporting delays +## Find the reporting delay + +What it is? +With which other is related? The natural history of a disease influence the transmission dynamics. For `{EpiNow2}`, we can specify relevant delay distributions like the [reporting delay](../learners/reference.md#reportingdelay): @@ -220,6 +229,9 @@ epinow_estimates <- epinow( ``` +## concept map + +update it from last epiparameter test ::::::::::::::::::::::::::::::::::::: keypoints From 5acce712ff42f9282f9eef6c2097d2ed026bb184 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 30 Nov 2023 13:10:22 +0000 Subject: [PATCH 037/130] add placeholder for describe cases episode --- config.yaml | 11 ++++++----- episodes/describe-cases.Rmd | 28 ++++++++++++++++++++++++++++ 2 files changed, 34 insertions(+), 5 deletions(-) create mode 100644 episodes/describe-cases.Rmd diff --git a/config.yaml b/config.yaml index b32f919a..e3ddcce7 100644 --- a/config.yaml +++ b/config.yaml @@ -14,7 +14,7 @@ carpentry: 'incubator' title: 'Outbreak analytics with R' # Date the lesson was created (YYYY-MM-DD, this is empty by default) -created: +created: # Comma-separated list of keywords for the lesson keywords: 'forecasts, epidemic models, interventions' @@ -58,9 +58,10 @@ contact: 'andree.valle-campos@lshtm.ac.uk' # - another-learner.md # Order of episodes in your lesson -episodes: +episodes: - introduction.Rmd - read-delays.Rmd +- describe-cases.Rmd - read-cases.Rmd - describe-data.Rmd - create-report.Rmd @@ -72,13 +73,13 @@ episodes: - compare-interventions.Rmd # Information for Learners -learners: +learners: # Information for Instructors -instructors: +instructors: # Learner Profiles -profiles: +profiles: # Customisation --------------------------------------------- # diff --git a/episodes/describe-cases.Rmd b/episodes/describe-cases.Rmd new file mode 100644 index 00000000..884d7a9d --- /dev/null +++ b/episodes/describe-cases.Rmd @@ -0,0 +1,28 @@ +--- +title: 'describe-cases' +teaching: 10 +exercises: 2 +--- + +:::::::::::::::::::::::::::::::::::::: questions + +- How do you visualize incidence cases? + +:::::::::::::::::::::::::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::: objectives + +- Create incidence curves + +:::::::::::::::::::::::::::::::::::::::::::::::: + +## Introduction + +This is a lesson created via The Carpentries Workbench. + +::::::::::::::::::::::::::::::::::::: keypoints + +- Use `{incidence2}` + +:::::::::::::::::::::::::::::::::::::::::::::::: + From 33fb92af06f7db10a8205bea05fb35ff3da1652e Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 30 Nov 2023 13:46:34 +0000 Subject: [PATCH 038/130] replace serial interval with generation time --- episodes/introduction.Rmd | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 7c64a28f..b5c5bf86 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -53,7 +53,7 @@ of interest ([Prism, 2016](http://prism.edu.au/publications/prism-modeling-guide - if $R>1$, the epidemic is likely to growth, - if $R<1$, the epidemic is likely to decline. -To estimate the reproduction number we need to collect __data inputs__ like the incidence of reported cases and the [serial interval](../learners/reference.md#serialinterval) distribution. Then, to get it, we need to apply the appropriate mathematical models written in code with the required computational methods. That is not enough! Following _good practices_, that code should be peer-reviewed and contain internal tests to double-check that we are getting the estimates we expect. Imagine rewriting all of it during a health emergency! +To estimate the reproduction number we can use two __data inputs__: the incidence of reported cases and the [generation time](../learners/reference.md#generationtime) distribution. Then, to get it, we need to apply the appropriate mathematical models written in code with the required computational methods. That is not enough! Following _good practices_, that code should be peer-reviewed and contain internal tests to double-check that we are getting the estimates we expect. Imagine rewriting all of it during a health emergency! In R, the fundamental unit of shareable code is the _package_. A package bundles together code, data, documentation, and tests, and is easy to share with others ([Wickham and Bryan, 2023](https://r-pkgs.org/introduction.html)). We, as epidemiologists, can contribute to their collaborative maintenance as a community, to perform less error-prone data analysis pipelines. @@ -88,14 +88,16 @@ Case incidence data needs be stored in a data frame with the observed number of example_confirmed ``` -### Then, set the delay distributions +### Then, set the generation time -The natural history of a disease influence the transmission dynamics. For `{EpiNow2}`, we can specify a relevant epidemiological distributions, the [generation time](../learners/reference.md#generationtime): +Not all primary cases have the same probability to generate a secondary case. The onset and cessation of infectiousness may occur gradually. For `{EpiNow2}`, we can specify it as a probability distribution: ```{r} generation_time <- dist_spec( - mean = 3.6, sd = 3.1, - max = 20, distribution = "lognormal" + mean = 3.6, + sd = 3.1, + max = 20, + distribution = "lognormal" ) ``` From c0d03e3eabb5804ccf4ea84255184f6442277a43 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 30 Nov 2023 13:49:34 +0000 Subject: [PATCH 039/130] add figure and references also, move serial interval definition as common proxy of generation time --- episodes/fig/delay-distribution-01.jpg | Bin 0 -> 24679 bytes episodes/fig/delay-distribution-02.jpg | Bin 0 -> 86987 bytes episodes/read-delays.Rmd | 6 +++++- 3 files changed, 5 insertions(+), 1 deletion(-) create mode 100644 episodes/fig/delay-distribution-01.jpg create mode 100644 episodes/fig/delay-distribution-02.jpg diff --git a/episodes/fig/delay-distribution-01.jpg b/episodes/fig/delay-distribution-01.jpg new file mode 100644 index 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zkg&e>q1KA#f~?qzxkaG!bj9?5FUqHH8=#8&n27;gFmh#U?x2SK!SQ5gExpF(4Mhys+}j`&ep1eyMZd zx^%WgLc0XQ?U0ZSTHskCKD}xLX-3n~1%9W?0;W5NFQT5qqhHgw@-;qAW0KmaB>T>L zQ?RgX6at$jc#EOk$?_E(9p*o3dbP%H3i&20b_IdnE*Gmqnr>-OVL!qa<=prLT3#yN zf>7qFyPNnqCs*gBjdh!$@dlJM+X&e_ z)`l2d{}qrJ-w$SFupwwu;cjH-M(6vO%; + ## Find a generation time What it is? With which other is related? -> the serial interval +[serial interval](../learners/reference.md#serialinterval) distribution + ```{r} ### 1 replace generation time from literature ----- From 92ed18292b7847749997c55e89e9dfc85a19252a Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 30 Nov 2023 14:00:30 +0000 Subject: [PATCH 040/130] expand argument detail in intro episode add variability to definition --- episodes/introduction.Rmd | 6 +++--- learners/reference.md | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index b5c5bf86..5824b56f 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -90,7 +90,7 @@ example_confirmed ### Then, set the generation time -Not all primary cases have the same probability to generate a secondary case. The onset and cessation of infectiousness may occur gradually. For `{EpiNow2}`, we can specify it as a probability distribution: +Not all primary cases have the same probability to generate a secondary case. The onset and cessation of infectiousness may occur gradually. For `{EpiNow2}`, we can specify it as a probability `distribution` with `mean`, standard deviation `sd`, and maximum value `max`: ```{r} generation_time <- dist_spec( @@ -103,13 +103,13 @@ generation_time <- dist_spec( ### Let's calculate the reproduction number! -In the `epinow()` function we can add the cases, delays, and your preferred computational parameters for this calculation: +In the `epinow()` function we can add the `reported_cases` data frame, the `generation_time` distribution, and your preferred computation `stan` parameters for this calculation: ```{r,message=FALSE,warning=FALSE,cache=TRUE} epinow_estimates <- epinow( # cases reported_cases = example_confirmed[1:60], - # delays + # distribution generation_time = generation_time_opts(generation_time), # computation stan = stan_opts( diff --git a/learners/reference.md b/learners/reference.md index d6ae325c..3c795d81 100644 --- a/learners/reference.md +++ b/learners/reference.md @@ -42,7 +42,7 @@ title: 'Glossary of Terms: Epiverse-TRACE' ## G [Generation time]{#generationtime} -: Distribution of time between infection of an index case and infection of its secondary cases. Most commonly, the generation time distribution is estimated from data on the [serial interval](#serialinterval) distribution of an infection ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). +: Distribution of time between infection of an index case and infection of its secondary cases. Most commonly, the generation time distribution is commonly estimated from data on the [serial interval](#serialinterval) distribution of an infection ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). [Growth rate]{#growth} : The exponential growth rate tells us how much cases are increasing or decreasing at the start of an epidemic. It gives us a measure of speed of transmission, see [Dushoff & Park, 2021](https://royalsocietypublishing.org/doi/full/10.1098/rspb.2020.1556). From 5255c4f50b681e76c40a64b18f3805f4f5839fe5 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 30 Nov 2023 16:15:44 +0000 Subject: [PATCH 041/130] update key points --- episodes/read-delays.Rmd | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 2e3101be..e2649350 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -239,10 +239,7 @@ update it from last epiparameter test ::::::::::::::::::::::::::::::::::::: keypoints -- Use `.md` files for episodes when you want static content -- Use `.Rmd` files for episodes when you need to generate output -- Run `sandpaper::check_lesson()` to identify any issues with your lesson -- Run `sandpaper::build_lesson()` to preview your lesson locally +- Use `{epiparameter}` :::::::::::::::::::::::::::::::::::::::::::::::: From e4d1c3d8ecdfddc06d7fd1a9ae923bddeaf276ad Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 2 Dec 2023 15:19:35 -0500 Subject: [PATCH 042/130] replace license file content to epiverse-trace --- LICENSE.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/LICENSE.md b/LICENSE.md index 49a8cbdd..f9188a45 100644 --- a/LICENSE.md +++ b/LICENSE.md @@ -4,7 +4,7 @@ title: "Licenses" ## Instructional Material -The tutorials in this repository are developed by Epiverse-TRACE, based on the [lesson template from the Carpentries](https://github.com/carpentries/workbench-template-rmd) (template under CC BY license). +The lessons in this repository are developed by Epiverse-TRACE, based on the [lesson template from the Carpentries](https://github.com/carpentries/workbench-template-rmd) (template under CC BY license). All Epiverse-TRACE instructional material is made available under the [Creative Commons From fa1fd075c1e0cc6eba1abf8b759c222d915858c5 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 2 Dec 2023 15:27:29 -0500 Subject: [PATCH 043/130] amend change in license by error --- LICENSE.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/LICENSE.md b/LICENSE.md index f9188a45..49a8cbdd 100644 --- a/LICENSE.md +++ b/LICENSE.md @@ -4,7 +4,7 @@ title: "Licenses" ## Instructional Material -The lessons in this repository are developed by Epiverse-TRACE, based on the [lesson template from the Carpentries](https://github.com/carpentries/workbench-template-rmd) (template under CC BY license). +The tutorials in this repository are developed by Epiverse-TRACE, based on the [lesson template from the Carpentries](https://github.com/carpentries/workbench-template-rmd) (template under CC BY license). All Epiverse-TRACE instructional material is made available under the [Creative Commons From 94ae38acfe44447b99d99d61f28062275496eb35 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 2 Dec 2023 15:30:47 -0500 Subject: [PATCH 044/130] Revert "amend change in license by error" This reverts commit 6f7bdca5e4068db3b533a37db27011fa92912c40. --- LICENSE.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/LICENSE.md b/LICENSE.md index 49a8cbdd..f9188a45 100644 --- a/LICENSE.md +++ b/LICENSE.md @@ -4,7 +4,7 @@ title: "Licenses" ## Instructional Material -The tutorials in this repository are developed by Epiverse-TRACE, based on the [lesson template from the Carpentries](https://github.com/carpentries/workbench-template-rmd) (template under CC BY license). +The lessons in this repository are developed by Epiverse-TRACE, based on the [lesson template from the Carpentries](https://github.com/carpentries/workbench-template-rmd) (template under CC BY license). All Epiverse-TRACE instructional material is made available under the [Creative Commons From d760939c9486b3d4698f4bb5670b2c1b2447cd18 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 2 Dec 2023 15:34:00 -0500 Subject: [PATCH 045/130] Revert "replace license file content to epiverse-trace" This reverts commit a6e9647a405f4fd1f8419fcba3488a311f66ca6a. --- LICENSE.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/LICENSE.md b/LICENSE.md index f9188a45..49a8cbdd 100644 --- a/LICENSE.md +++ b/LICENSE.md @@ -4,7 +4,7 @@ title: "Licenses" ## Instructional Material -The lessons in this repository are developed by Epiverse-TRACE, based on the [lesson template from the Carpentries](https://github.com/carpentries/workbench-template-rmd) (template under CC BY license). +The tutorials in this repository are developed by Epiverse-TRACE, based on the [lesson template from the Carpentries](https://github.com/carpentries/workbench-template-rmd) (template under CC BY license). All Epiverse-TRACE instructional material is made available under the [Creative Commons From b7c2465d3764ae3a6b3b3a28fa43074ebe4d8b97 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 2 Dec 2023 15:37:38 -0500 Subject: [PATCH 046/130] add callout draft for delays from data --- episodes/read-delays.Rmd | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index e2649350..55fb5cf3 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -233,6 +233,12 @@ epinow_estimates <- epinow( ``` +## From our data! (callout) + +estimate serial interval from case and contact data pairs + +ref: https://www.reconlearn.org/post/real-time-response-2 + ## concept map update it from last epiparameter test From 18fec374c9a5abf5930461ce24c5411461dd23ea Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 2 Dec 2023 15:39:38 -0500 Subject: [PATCH 047/130] add placeholder to show how to get delays from contact data --- episodes/describe-cases.Rmd | 2 ++ 1 file changed, 2 insertions(+) diff --git a/episodes/describe-cases.Rmd b/episodes/describe-cases.Rmd index 884d7a9d..40448990 100644 --- a/episodes/describe-cases.Rmd +++ b/episodes/describe-cases.Rmd @@ -7,12 +7,14 @@ exercises: 2 :::::::::::::::::::::::::::::::::::::: questions - How do you visualize incidence cases? +- How to get serial interval from case data? :::::::::::::::::::::::::::::::::::::::::::::::: ::::::::::::::::::::::::::::::::::::: objectives - Create incidence curves +- Estimate serial interval distribution from contact data :::::::::::::::::::::::::::::::::::::::::::::::: From a8c177605bb861732d0907eae769667eff991c26 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Wed, 6 Dec 2023 21:01:18 -0500 Subject: [PATCH 048/130] add period delay figures --- episodes/fig/disease-reporting.jpg | Bin 0 -> 44277 bytes .../fig/incubation-period-serial-interval.jpg | Bin 0 -> 231856 bytes episodes/fig/infectiousness-covid19.jpg | Bin 0 -> 720176 bytes episodes/fig/serial-interval-covid-sars.jpg | Bin 0 -> 122860 bytes episodes/fig/time-periods.jpg | Bin 0 -> 300672 bytes episodes/read-delays.Rmd | 12 ++++++++++++ 6 files changed, 12 insertions(+) create mode 100644 episodes/fig/disease-reporting.jpg create mode 100644 episodes/fig/incubation-period-serial-interval.jpg create mode 100644 episodes/fig/infectiousness-covid19.jpg create mode 100644 episodes/fig/serial-interval-covid-sars.jpg create mode 100644 episodes/fig/time-periods.jpg diff --git a/episodes/fig/disease-reporting.jpg 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zBH`DSebD!`H>xIJe&I<=+%S%cb{c#5u6*#o`-jaAkH53bEdBPKWqBZ~>sw5P9!eB> zJ+jW0tBoA~K89XxpB`1E{m1?Bk8R;VY?vKsH9cg%t}ODm6HE%JZr?Mo#o#T$?rSGs zu$;%$CG1Rjjg6l6**Wo&8^R_T==QMCNOV76cu$qpfBZI){GEkhI<);TCi2%&w*Dw% zI??`RxcIWP78Q6@KDeY1^zCZ0M7$k?$?v=>t|#4TH5HGh$zaK#%>zh0=m`j@hgfOKil6p=8P*mKDAx`>LwFz^LlxQC&mPmjgkB`rXgD=pYb6N3Zj&XS=v9ncV75Bb$FMzQ zrA!bFHV3X&v6~yk#T#^E;vWiBOMvNHJ{s6oSb@E+J6sHSS}pqZj7VM_`PvQ z*WQjHU($`>U#xCckcVC#Fg_DY>TlthA9%evm@EIm@-Gr$B5vlCp;qz^Cjx`1n1mHqGPp$u(4)dXn^j zbww0u)gsUYg3(P$+^q~u%xK1DJ#9R6W8%m=8S#lA#dr-$rx*z2 zb-omAJAb+S9{6^jg?0zUrV(#6Qq|=*3d~kAbpb&_LYHo3UjC?Pd$?S#1DjT+pWfSR zZqd`zJ`y5DG5GQ|ajkQ!OW4^jy=7CO zw87C$o|z(H&;@ePYH?dT5~c_a((wa#j!3MP7t6!XEtt0j;_i46-x&2~S!Y@$;k8uP zR2B_5vHK=y8x&+LUoPKVQvWymlN)`%8Jp(y#cNPAqUWY<2{)k!9kIYY6= +![Definition of key time periods. From [Xiang et al, 2021](https://www.sciencedirect.com/science/article/pii/S2468042721000038)](fig/time-periods.jpg) + +![Window and kinetics of SARS-CoV-2 infectiousness in recently infected contacts. From [Hakki et al, 2022](https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(22)00226-0/fulltext)](fig/infectiousness-covid19.jpg) + ## Find a generation time What it is? @@ -37,6 +41,8 @@ With which other is related? -> the serial interval [serial interval](../learners/reference.md#serialinterval) distribution +![Serial interval of novel coronavirus (COVID-19) infections. From [Nishiura et al, 2020](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext)](fig/serial-interval-covid-sars.jpg) + ```{r} ### 1 replace generation time from literature ----- @@ -105,6 +111,8 @@ With which other is related? The natural history of a disease influence the transmission dynamics. For `{EpiNow2}`, we can specify relevant delay distributions like the [incubation period](../learners/reference.md#incubation): +![The relationship between the incubation period and serial interval. From [Nishiura 2020](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext)](fig/incubation-period-serial-interval.jpg) + ```{r} incubation_period <- dist_spec( mean = 4, sd = 2, @@ -197,6 +205,8 @@ With which other is related? The natural history of a disease influence the transmission dynamics. For `{EpiNow2}`, we can specify relevant delay distributions like the [reporting delay](../learners/reference.md#reportingdelay): +![Timeline for chain of disease reporting, the Netherlands. Lab, laboratory; PHA, public health authority. From [Marinović et al., 2015](https://wwwnc.cdc.gov/eid/article/21/2/13-0504_article)](fig/disease-reporting.jpg) + ```{r} reporting_delay <- dist_spec( mean = convert_to_logmean(2, 1), @@ -239,6 +249,8 @@ estimate serial interval from case and contact data pairs ref: https://www.reconlearn.org/post/real-time-response-2 +ref: to epimodelac + ## concept map update it from last epiparameter test From 25d0a81d8f2b5768442e135760f1ee2b299f8621 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Wed, 6 Dec 2023 21:01:38 -0500 Subject: [PATCH 049/130] edit text after review --- episodes/introduction.Rmd | 22 +++++++++++++--------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 5824b56f..bddc9cb4 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -67,14 +67,14 @@ Remember your last experience with outbreak data and reflect in these questions: - How did you get accessed to that data? - Is that analysis pipeline you followed reusable for a next response? -Pair up and share your experiences. +Reflect on your experiences. ::::::::::::::::::::::::::::::::::::::::::: ## Example: Quantify transmission -The `{EpiNow2}` package provide us with a three-steps solution to _quantify the transmissibility_. +The `{EpiNow2}` package provide us with a three-steps solution to _quantify the transmissibility_. Let's see how to do this with a minimal example. First, load the package: ```{r,warning=FALSE} library(EpiNow2) @@ -82,7 +82,7 @@ library(EpiNow2) ### First, get your case data -Case incidence data needs be stored in a data frame with the observed number of cases per day: +Case incidence data needs be stored in a data frame with the observed number of cases per day. We can read example from the package: ```{r} example_confirmed @@ -103,7 +103,11 @@ generation_time <- dist_spec( ### Let's calculate the reproduction number! -In the `epinow()` function we can add the `reported_cases` data frame, the `generation_time` distribution, and your preferred computation `stan` parameters for this calculation: +In the `epinow()` function we can add: + +- the `reported_cases` data frame, +- the `generation_time` distribution, and +- the computation `stan` parameters for this calculation: ```{r,message=FALSE,warning=FALSE,cache=TRUE} epinow_estimates <- epinow( @@ -119,7 +123,7 @@ epinow_estimates <- epinow( ) ``` -Getting the time-varying (or [effective](../learners/reference.md#effectiverepro)) reproduction number next to the cases by date of report and cases by date of infection: +As an output, we get the time-varying (or [effective](../learners/reference.md#effectiverepro)) reproduction number, as well as the cases by date of report and date of infection: ```{r} base::plot(epinow_estimates) @@ -149,7 +153,7 @@ So, all these tasks can be interconnected in a pipeline: ## What can we do? -Our strategy is to gradually incorporate specialized R packages into the traditional analysis pipeline. These packages should fill the gaps in these epidemiology-specific tasks in response to outbreaks. +Our strategy is to gradually incorporate specialized R packages into our traditional analysis pipeline. These packages should fill the gaps in these epidemiology-specific tasks in response to outbreaks. Epiverse-TRACE aim is to provide a software ecosystem for outbreak analytics. We support the development of software pieces, make the existing ones interoperable for the user experience, and stimulate a community of practice. @@ -159,11 +163,11 @@ Epiverse-TRACE aim is to provide a software ecosystem for outbreak analytics. We Our plan for these tutorials is to introduce key solutions from packages in all the tasks before and after the _Quantify transmission_ task, plus the required theory concepts to interpret modelling outputs and make rigorous conclusions. -- In the first set of episodes, you will learn how to optimize the reading and cleaning of epidemiological parameters and case data to input them into the _Quantify transmission_ task. These preliminary tasks are the __Early tasks__. Packages here are `{readepi}`, `{cleanepi}`, `{linelist}`, `{epiparameter}`, and `{episoap}`. +- In the first set of episodes, you will learn how to optimize the reading and cleaning of epidemiological parameters and case data to input them into the _Quantify transmission_ task. These preliminary tasks are the __Early tasks__. This includes package like `{readepi}`, `{cleanepi}`, `{linelist}`, `{epiparameter}`, and `{episoap}`. -- Then, we will get deeper into the packages and required theory to _Quantify transmission_ and perform more real-time analysis tasks next to it. These are the __Middle tasks__. Packages here are `{EpiNow2}`, `{cfr}`, `{epichains}`, and `{superspreading}`. +- Then, we will get deeper into the packages and required theory to _Quantify transmission_ and perform more real-time analysis tasks next to it. These are the __Middle tasks__. This includes `{EpiNow2}`, `{cfr}`, `{epichains}`, and `{superspreading}`. -- Lastly, we will use _Quantify transmission_ data outputs to compare it to other indicators and simulate epidemic scenarios as part of the __Late tasks__. Packages here are `{finalsize}`, `{epidemics}`, `{scenarios}`, and `{vaccineff}`. +- Lastly, we will use _Quantify transmission_ data outputs to compare it to other indicators and simulate epidemic scenarios as part of the __Late tasks__. This includes `{finalsize}`, `{epidemics}`, and `{scenarios}`. ::::::::::::::::::::::::::::::::::::: keypoints From 3980291a55409ff4fb206064d138c048753ed5e2 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Wed, 6 Dec 2023 21:02:05 -0500 Subject: [PATCH 050/130] add symptom onset for reporting delay definition --- learners/reference.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/learners/reference.md b/learners/reference.md index 3c795d81..abaed21c 100644 --- a/learners/reference.md +++ b/learners/reference.md @@ -90,7 +90,7 @@ title: 'Glossary of Terms: Epiverse-TRACE' ## R [Reporting delay]{#reportingdelay} -: Delay between the time an event occurs and the time it is reported ([Lawless, 1994](https://www.jstor.org/stable/3315820)). We can quantify it by comparing the linelist with successive versions of it or up-to-date reported aggregated case counts ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). +: Delay or lag between the time an event occurs (e.g. symptom onset) and the time it is reported ([Lawless, 1994](https://www.jstor.org/stable/3315820)). We can quantify it by comparing the linelist with successive versions of it or up-to-date reported aggregated case counts ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). ## S From ad5b08c91c053cb2c238be61c3017b73bf8bb5af Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Wed, 6 Dec 2023 22:37:54 -0500 Subject: [PATCH 051/130] remove figures replaced in previous commit https://github.com/epiverse-trace/tutorials/commit/d70635db660547469394a4158b00ae979fe9f194 --- episodes/fig/delay-distribution-01.jpg | Bin 24679 -> 0 bytes episodes/fig/delay-distribution-02.jpg | Bin 86987 -> 0 bytes 2 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 episodes/fig/delay-distribution-01.jpg delete mode 100644 episodes/fig/delay-distribution-02.jpg diff --git a/episodes/fig/delay-distribution-01.jpg b/episodes/fig/delay-distribution-01.jpg deleted file mode 100644 index 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zkg&e>q1KA#f~?qzxkaG!bj9?5FUqHH8=#8&n27;gFmh#U?x2SK!SQ5gExpF(4Mhys+}j`&ep1eyMZd zx^%WgLc0XQ?U0ZSTHskCKD}xLX-3n~1%9W?0;W5NFQT5qqhHgw@-;qAW0KmaB>T>L zQ?RgX6at$jc#EOk$?_E(9p*o3dbP%H3i&20b_IdnE*Gmqnr>-OVL!qa<=prLT3#yN zf>7qFyPNnqCs*gBjdh!$@dlJM+X&e_ z)`l2d{}qrJ-w$SFupwwu;cjH-M(6vO%; Date: Wed, 6 Dec 2023 22:38:16 -0500 Subject: [PATCH 052/130] add infectiousness to glossary --- episodes/introduction.Rmd | 2 +- episodes/read-delays.Rmd | 4 +--- learners/reference.md | 3 +++ 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index bddc9cb4..ef31f520 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -90,7 +90,7 @@ example_confirmed ### Then, set the generation time -Not all primary cases have the same probability to generate a secondary case. The onset and cessation of infectiousness may occur gradually. For `{EpiNow2}`, we can specify it as a probability `distribution` with `mean`, standard deviation `sd`, and maximum value `max`: +Not all primary cases have the same probability to generate a secondary case. The onset and cessation of [infectiousness](../learners/reference.md#infectiousness) may occur gradually. For `{EpiNow2}`, we can specify it as a probability `distribution` with `mean`, standard deviation `sd`, and maximum value `max`: ```{r} generation_time <- dist_spec( diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index b6380856..4eded8df 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -32,8 +32,6 @@ The natural history of a disease influence the transmission dynamics. ![Definition of key time periods. From [Xiang et al, 2021](https://www.sciencedirect.com/science/article/pii/S2468042721000038)](fig/time-periods.jpg) -![Window and kinetics of SARS-CoV-2 infectiousness in recently infected contacts. From [Hakki et al, 2022](https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(22)00226-0/fulltext)](fig/infectiousness-covid19.jpg) - ## Find a generation time What it is? @@ -249,7 +247,7 @@ estimate serial interval from case and contact data pairs ref: https://www.reconlearn.org/post/real-time-response-2 -ref: to epimodelac +ref: https://epiverse-trace.github.io/epimodelac/EnfermedadX.html ## concept map diff --git a/learners/reference.md b/learners/reference.md index abaed21c..877bdfd8 100644 --- a/learners/reference.md +++ b/learners/reference.md @@ -60,6 +60,9 @@ title: 'Glossary of Terms: Epiverse-TRACE' [Initial conditions]{#initial} : In [ODEs](#ordinary), the initial conditions are the values of the state variables at the start of the model simulation (at time 0). For example, if there is one infectious individual in a population of 1000 in an Susceptible-Infectious-Recovered model, the initial conditions would be $S(0) = 999$, $I(0) = 1$, $R(0) = 0$. +[Infectiousness]{#infectiousness} +: Median duration of the time between the onset and end of infectious viral shedding. +![Window and kinetics of SARS-CoV-2 infectiousness in recently infected contacts. 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+42,7 @@ title: 'Glossary of Terms: Epiverse-TRACE' ## G [Generation time]{#generationtime} -: Distribution of time between infection of an index case and infection of its secondary cases. Most commonly, the generation time distribution is commonly estimated from data on the [serial interval](#serialinterval) distribution of an infection ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). +: Time between infection of an index case and infection of its secondary cases. Most commonly, the generation time distribution is commonly estimated from data on the [serial interval](#serialinterval) distribution of an infection ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). [Growth rate]{#growth} : The exponential growth rate tells us how much cases are increasing or decreasing at the start of an epidemic. It gives us a measure of speed of transmission, see [Dushoff & Park, 2021](https://royalsocietypublishing.org/doi/full/10.1098/rspb.2020.1556). @@ -52,7 +52,7 @@ title: 'Glossary of Terms: Epiverse-TRACE' ## I [Incubation period]{#incubation} -: The time between becoming infected and the onset of symptoms. [More information on the incubation period](https://en.wikipedia.org/wiki/Latent_period_(epidemiology)#Incubation_period). +: The time between becoming infected and the onset of symptoms. [More information on the incubation period](https://en.wikipedia.org/wiki/Latent_period_(epidemiology)#Incubation_period). This can be different to the [latent period](#latent). ([Xiang et al, 2021](https://www.sciencedirect.com/science/article/pii/S2468042721000038)) [Indirect transmission]{#indirect} : Indirectly transmitted infections are passed on to humans via contact with vectors, animals or contaminated environment. Vector-borne infections, zoonoses and water-borne infections are modelled as indirectly transmitted. @@ -61,7 +61,9 @@ title: 'Glossary of Terms: Epiverse-TRACE' : In [ODEs](#ordinary), the initial conditions are the values of the state variables at the start of the model simulation (at time 0). For example, if there is one infectious individual in a population of 1000 in an Susceptible-Infectious-Recovered model, the initial conditions would be $S(0) = 999$, $I(0) = 1$, $R(0) = 0$. [Infectiousness]{#infectiousness} -: Median duration of the time between the onset and end of infectious viral shedding. +: Also known as Duration of infectiousness. Time period between the onset and end of infectious [viral shedding](#viralshedding). Viral load and detection of infectious virus are the two key parameters for estimating infectiousness ([Puhach et al., 2022](https://www.nature.com/articles/s41579-022-00822-w)). + + ![Window and kinetics of SARS-CoV-2 infectiousness in recently infected contacts. From [Hakki et al, 2022](https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(22)00226-0/fulltext)](fig/infectiousness-covid19.jpg) @@ -71,7 +73,7 @@ title: 'Glossary of Terms: Epiverse-TRACE' ## L [Latent period]{#latent} -: The time between becoming infected and the onset of infectiousness. [More information on the latent period](https://en.wikipedia.org/wiki/Latent_period_(epidemiology)). +: The time between becoming infected and the onset of infectiousness. [More information on the latent period](https://en.wikipedia.org/wiki/Latent_period_(epidemiology)). This can be different to the [incubation period](#incubation). ([Xiang et al, 2021](https://www.sciencedirect.com/science/article/pii/S2468042721000038)) ## M [Model parameters (ODEs)]{#parsode} @@ -85,6 +87,10 @@ title: 'Glossary of Terms: Epiverse-TRACE' ## O [Ordinary differential equations]{#ordinary} : Ordinary differential equations (ODEs) can be used to represent the rate of change of one variable (e.g. number of infected individuals) with respect to another (e.g. time). Check out this introduction to [ODEs](https://mathinsight.org/ordinary_differential_equation_introduction). ODEs are widely used in infectious disease modelling to model the flow of individuals between different disease states. +[Natural history of disease]{#naturalhistory} +: Refers to the development of disease from beginning to end without any treatment or intervention. In fact, given the harmfulness of an epidemic, treatment or intervention measures are inevitable. Therefore, it is difficult for the natural history of a disease to be unaffected by the various coupling factors. ([Xiang et al, 2021](https://www.sciencedirect.com/science/article/pii/S2468042721000038)) + + @@ -116,6 +122,9 @@ title: 'Glossary of Terms: Epiverse-TRACE' [Vector-borne transmission]{#vectorborne} : Vector-borne transmission means an infection can be passed from a vector (e.g. mosquitoes) to humans. Examples of vector-borne diseases include malaria and dengue. The World Health Organization have a [Fact sheet about Vector-borne diseases](https://www.who.int/news-room/fact-sheets/detail/vector-borne-diseases) with key information and a list of them according to their vector. +[Viral shedding]{#viralshedding} +: The process of releasing a virus from a cell or body into the environment where it can infect other people. ([Cambridge Dictionary, 2023](https://dictionary.cambridge.org/us/dictionary/english/shedding)) + From 5f51ee3180770c57b4b10a937ae036c1dfe5c434 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 7 Dec 2023 02:10:38 -0500 Subject: [PATCH 055/130] add figures and text for first section --- episodes/read-delays.Rmd | 121 ++++++++++++++++++++++++++++++++++++--- 1 file changed, 113 insertions(+), 8 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 4eded8df..ea05f791 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -24,22 +24,101 @@ exercises: 2 :::::::::::::::::::::::::::::::::::::::::::::::: +::::::::::::::::::::::::::::::::::::: prereq + +## Prerequisites + +This episode requires you to be familiar with: + +**Data science** : Basic programming with R. + +**Epidemic theory** : Epidemiological parameters. Time periods. + +This also requires the following package: + +```r +if(!require("pak")) install.packages("pak") +pak::pak("epiverse-trace/epiparameter") +``` + +::::::::::::::::::::::::::::::::: + ## Systematic review of Parameters -The natural history of a disease influence the transmission dynamics. - - +The [natural history](../learners/reference.md#naturalhistory) of a infectious disease show us that its development has a regularity from stage to stage. The time periods from a infectious disease inform about the timing of transmission and interventions. ![Definition of key time periods. From [Xiang et al, 2021](https://www.sciencedirect.com/science/article/pii/S2468042721000038)](fig/time-periods.jpg) + +::::::::::::::::: callout + +### Definitions + +Take a look at the [glossary](../learners/reference.md) for all the definitions of the time periods of the figure above! + +::::::::::::::::::::::::: + +However, early in an epidemic, modelling efforts can be delayed by the lack of a centralized resource summarizing input parameters for the disease of interest ([Nash et al., 2023](https://mrc-ide.github.io/epireview/)). Projects like `{epiparameter}` and `{epireview}` are building online catalogs that can help building models faster for coming outbreaks and epidemics from known and unknown pathogens. + + + +Let's explore how we can access to time periods and delays using `{epiparameter}` to plug them in to `{EpiNow2}`: + +```{r} +library(epiparameter) +library(EpiNow2) +``` + + ## Find a generation time -What it is? -With which other is related? -> the serial interval +The generation time, jointly with the R0, can inform about the speed of spread and its feasibility of control. Given a $R>1$, with a shorter generation time, cases can appear more quickly. + +![From the video by MRC Centre for Global Infectious Disease Analysis, Ep 76. Science In Context - Epi Parameter Review Group with Dr Anne Cori (27-07-2023) at ](fig/reproduction-generation-time.png) + + + + + + + +In calculating the effective reproductive number ($Rt$), the generation time distribution is often approximated by the [serial interval](../learners/reference.md#serialinterval) distribution although this can produced biased estimates. ([Knight et al., 2020](https://www.sciencedirect.com/science/article/pii/S2468042720300634?via%3Dihub)) + +::::::::::::::::::::::::::::::::: challenge -[serial interval](../learners/reference.md#serialinterval) distribution +### REVIEW + +Let's assume that COVID-19 and SARS has close Reproduction number values. Which one would be harder to control? Why do you conclude that? + +![Serial interval of novel coronavirus (COVID-19) infections overlaid with a published distribution of SARS. From [Nishiura et al, 2020](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext)](fig/serial-interval-covid-sars.jpg) + +::::::::::::::::: hint + +### HINT + +The peak of each curve can inform you about the mean each distribution. Larger mean, larger serial interval. + +:::::::::::::::::::::: + +::::::::::::::::: solution + +### SOLUTION + +Which one would be harder to control? + +- COVID-19 + +Why do you conclude that? + +- COVID-19 has the lowest mean serial interval. The approximate mean value for the serial interval of: + - COVID-19 is around 4 days. + - SARS us around 7 days. + +:::::::::::::::::::::::::: + + +::::::::::::::::::::::::::::::::::::::::::: -![Serial interval of novel coronavirus (COVID-19) infections. From [Nishiura et al, 2020](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext)](fig/serial-interval-covid-sars.jpg) ```{r} ### 1 replace generation time from literature ----- @@ -101,7 +180,6 @@ base::plot(epinow_estimates) # if not, find serial interval and use it as serial interval ``` - ## Find the incubation time What it is? @@ -111,6 +189,17 @@ The natural history of a disease influence the transmission dynamics. For `{EpiN ![The relationship between the incubation period and serial interval. From [Nishiura 2020](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext)](fig/incubation-period-serial-interval.jpg) +![Parameter estimates. Plausible ranges for the key parameters R0 and θ (see main text for sources) for four viral infections of public concern are shown as shaded regions. The size of the shaded area reflects the uncertainties in the parameter estimates. [Fraser et al., 2004](https://www.pnas.org/doi/10.1073/pnas.0307506101)](fig/reproduction-number-pre-symptomatic.png) + + + + + + + + + + ```{r} incubation_period <- dist_spec( mean = 4, sd = 2, @@ -195,12 +284,20 @@ base::plot(epinow_estimates) ``` +Potential biases like + +- censoring bias (short periods) +- reporting bias (recent contacts) ## Find the reporting delay What it is? With which other is related? +Adjusting for delays + +![Rt is a measure of transmission at time t. Observations after time t must be adjusted. ICU, intensive care unit. From [Gostic et al., 2020](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409#sec007)](fig/rt-adjusting-delays.png) + The natural history of a disease influence the transmission dynamics. For `{EpiNow2}`, we can specify relevant delay distributions like the [reporting delay](../learners/reference.md#reportingdelay): ![Timeline for chain of disease reporting, the Netherlands. Lab, laboratory; PHA, public health authority. From [Marinović et al., 2015](https://wwwnc.cdc.gov/eid/article/21/2/13-0504_article)](fig/disease-reporting.jpg) @@ -241,8 +338,16 @@ epinow_estimates <- epinow( ``` +## Severity parameters + +The three examples above represent transmission parameters. + +- Duration of hospitalization (bed capacity needed) + ## From our data! (callout) +Fit a parametric distribution to estimate the mean and sd + estimate serial interval from case and contact data pairs ref: https://www.reconlearn.org/post/real-time-response-2 From 3ea7742a9ee8810685e9e58bbeec8deefad36697 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 7 Dec 2023 23:41:43 -0500 Subject: [PATCH 056/130] add callout on reminder about the adjustments for Rt --- episodes/introduction.Rmd | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index ef31f520..e0ff5cf8 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -129,6 +129,14 @@ As an output, we get the time-varying (or [effective](../learners/reference.md#e base::plot(epinow_estimates) ``` +::::::::::::::::: callout + +### Is this $Rt$ estimation biased? + +Review [Gostic et al., 2020](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409) about what additional adjustments this estimation requires to avoid false precision in $Rt$. + +::::::::::::::::::::::::: + ## The problem! However, _quantifying the transmissibility_ during a real-life outbreak response is more challenging than this example! From 55a0b80dba7441148482dc31f5494d2990852e6e Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 7 Dec 2023 23:42:24 -0500 Subject: [PATCH 057/130] add episode et1 content --- episodes/read-delays.Rmd | 38 +++++++++++++++++++++++++++++--------- 1 file changed, 29 insertions(+), 9 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index ea05f791..8f8c3eaf 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -2,6 +2,8 @@ title: 'Describe case data' teaching: 10 exercises: 2 +editor_options: + chunk_output_type: console --- :::::::::::::::::::::::::::::::::::::: questions @@ -67,6 +69,7 @@ Let's explore how we can access to time periods and delays using `{epiparameter} ```{r} library(epiparameter) library(EpiNow2) +library(tidyverse) ``` @@ -88,7 +91,12 @@ In calculating the effective reproductive number ($Rt$), the generation time dis ### REVIEW -Let's assume that COVID-19 and SARS has close Reproduction number values. Which one would be harder to control? Why do you conclude that? +Let's assume that COVID-19 and SARS has similar Reproduction number values. + +Given the Serial Interval of both infections in the figure below: + +- Which one would be harder to control? +- Why do you conclude that? ![Serial interval of novel coronavirus (COVID-19) infections overlaid with a published distribution of SARS. From [Nishiura et al, 2020](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext)](fig/serial-interval-covid-sars.jpg) @@ -96,7 +104,7 @@ Let's assume that COVID-19 and SARS has close Reproduction number values. Which ### HINT -The peak of each curve can inform you about the mean each distribution. Larger mean, larger serial interval. +The peak of each curve can inform you about the location of the mean of each distribution. The larger the mean, the larger the serial interval. :::::::::::::::::::::: @@ -110,35 +118,47 @@ Which one would be harder to control? Why do you conclude that? -- COVID-19 has the lowest mean serial interval. The approximate mean value for the serial interval of: - - COVID-19 is around 4 days. - - SARS us around 7 days. +- COVID-19 has the lowest mean serial interval. The approximate mean value for the serial interval of COVID-19 is around 4 days, and SARS is around 7 days. Thus, COVID-19 is likely to have newer generations in less time than SARS. + :::::::::::::::::::::::::: ::::::::::::::::::::::::::::::::::::::::::: +Now, instead of using `EpiNow2::dist_spec()` to plug-in numeric values to specify the distribution: ```{r} -### 1 replace generation time from literature ----- - generation_time <- dist_spec( mean = 3.6, sd = 3.1, max = 20, distribution = "lognormal" ) +``` -# replace this assuming that this is covid -# with lognormal +Assuming that the disease we are dealing with is COVID, We can replace this code chunk and extract the Serial Interval for COVID-19 using the `epiparameter::epidist_db()` function. +```{r} +epiparameter::epidist_db( + disease = "covid", + epi_dist = "serial", + author = "Nishiura") +``` + +We have more than one epidemiological delay. To summarize this view and get the column names from the underlying parameter dataset we connect the previous code chunk with `epiparameter::list_distributions()`: + +```{r} epiparameter::epidist_db( disease = "covid", epi_dist = "serial", author = "Nishiura") %>% epiparameter::list_distributions() +``` + +If we want to filter only one +```{r} covid_lnorm <- epiparameter::epidist_db( disease = "covid", epi_dist = "serial", From 5661d7b56f0fa6d68a6c69b830a5c895de50ef6b Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sun, 10 Dec 2023 21:38:28 -0500 Subject: [PATCH 058/130] fix and extend the definitions --- learners/reference.md | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/learners/reference.md b/learners/reference.md index 8b5c5ee6..79bd9c3d 100644 --- a/learners/reference.md +++ b/learners/reference.md @@ -35,14 +35,15 @@ title: 'Glossary of Terms: Epiverse-TRACE' ## E [Effective reproduction number]{#effectiverepro} -: The effective reproduction number ($Rt$) is similar to the [Basic reproductive number](#basic) ($R0$), but $Rt$ measures the number of persons infected by infectious person when some portion of the population has already been infected. Read more about the [etymology of Reproduction number by Sharma et al, 2023](https://wwwnc.cdc.gov/eid/article/29/8/22-1445_article). +: The time-varying or effective reproduction number ($Rt$) is similar to the [Basic reproductive number](#basic) ($R0$), but $Rt$ measures the number of persons infected by infectious person when some portion of the population has already been infected. Read more about the [etymology of Reproduction number by Sharma et al, 2023](https://wwwnc.cdc.gov/eid/article/29/8/22-1445_article). ## G [Generation time]{#generationtime} -: Time between infection of an index case and infection of its secondary cases. Most commonly, the generation time distribution is commonly estimated from data on the [serial interval](#serialinterval) distribution of an infection ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). +: Time between the onset of infectiousness of an index case and its secondary case. This always needs to be positive. +The generation time distribution is commonly estimated from data on the [serial interval](#serialinterval) distribution of an infection ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). [Growth rate]{#growth} : The exponential growth rate tells us how much cases are increasing or decreasing at the start of an epidemic. It gives us a measure of speed of transmission, see [Dushoff & Park, 2021](https://royalsocietypublishing.org/doi/full/10.1098/rspb.2020.1556). @@ -52,7 +53,10 @@ title: 'Glossary of Terms: Epiverse-TRACE' ## I [Incubation period]{#incubation} -: The time between becoming infected and the onset of symptoms. [More information on the incubation period](https://en.wikipedia.org/wiki/Latent_period_(epidemiology)#Incubation_period). This can be different to the [latent period](#latent). ([Xiang et al, 2021](https://www.sciencedirect.com/science/article/pii/S2468042721000038)) +: The time between becoming infected and the onset of symptoms. +[More information on the incubation period](https://en.wikipedia.org/wiki/Latent_period_(epidemiology)#Incubation_period). +This can be different to the [latent period](#latent) as shown in Figure 4 from ([Xiang et al. (2021)](https://www.sciencedirect.com/science/article/pii/S2468042721000038)). +The relationship between the incubation period and the [serial interval](#serialinterval) helps to define the type of infection transmission (symptomatic or pre-symptomatic). This is shown in Figure 2 at [Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext). [Indirect transmission]{#indirect} : Indirectly transmitted infections are passed on to humans via contact with vectors, animals or contaminated environment. Vector-borne infections, zoonoses and water-borne infections are modelled as indirectly transmitted. @@ -73,7 +77,8 @@ title: 'Glossary of Terms: Epiverse-TRACE' ## L [Latent period]{#latent} -: The time between becoming infected and the onset of infectiousness. [More information on the latent period](https://en.wikipedia.org/wiki/Latent_period_(epidemiology)). This can be different to the [incubation period](#incubation). ([Xiang et al, 2021](https://www.sciencedirect.com/science/article/pii/S2468042721000038)) +: The time between becoming infected and the onset of infectiousness. [More information on the latent period](https://en.wikipedia.org/wiki/Latent_period_(epidemiology)). +This can be different to the [incubation period](#incubation) as shown in Figure 4 from ([Xiang et al, 2021](https://www.sciencedirect.com/science/article/pii/S2468042721000038)) ## M [Model parameters (ODEs)]{#parsode} @@ -107,7 +112,10 @@ title: 'Glossary of Terms: Epiverse-TRACE' : The state variables in a model represented by [ordinary differential equations](#ordinary) are the disease states that individuals can be in e.g. if individuals can be susceptible, infectious or recovered the state variables are $S$, $I$ and $R$. There is an ordinary differential equation for each state variable. [Serial interval]{#serialinterval} -: The time delay between the onset of symptoms between a primary case and a secondary case. The serial interval is related to the [incubation period](#incubation), as described in Figure 2 at [Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext). +: The time delay between the onset of symptoms between a primary case and a secondary case. +This can be negative when pre-symptomatic infection occurs. +Most commonly, the serial interval distribution of an infection is used to estimate the [generation time](#generationtime) distribution ([(Cori et al., 2017)](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). +The relationship between the serial interval and the [incubation period](#incubation) helps to define the type of infection transmission (symptomatic or pre-symptomatic). This is shown in Figure 2 at [Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext). [Stochastic model]{#stochastic} : A model that includes some stochastic process resulting in variation in model simulations for the same initial conditions and parameter values. Examples include stochastic differential equations and branching process models. For more detail see [Allen (2017)](https://doi.org/10.1016/j.idm.2017.03.001). From 09709ed01dcd25098c730cfe55fcd4503eeeaf41 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sun, 10 Dec 2023 21:38:51 -0500 Subject: [PATCH 059/130] add objectives --- episodes/read-delays.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 8f8c3eaf..4f2c0991 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -21,8 +21,8 @@ editor_options: ::::::::::::::::::::::::::::::::::::: objectives -- Explain how to use markdown with the new lesson template -- Demonstrate how to include pieces of code, figures, and nested challenge blocks +- Get epidemological parameters from systematic reviews. +- Convert statistical summary to distribution parameters. :::::::::::::::::::::::::::::::::::::::::::::::: From 2c6413fcb0945dfa2f1c1b3d21e57f3b3187c056 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sun, 10 Dec 2023 21:40:46 -0500 Subject: [PATCH 060/130] add an explanation on serial interval biases with respect to the generation interval and pre-symptomatic infection --- episodes/read-delays.Rmd | 40 ++++++++++++++++++++++++++-------------- 1 file changed, 26 insertions(+), 14 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 4f2c0991..07c34e56 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -47,7 +47,7 @@ pak::pak("epiverse-trace/epiparameter") ## Systematic review of Parameters -The [natural history](../learners/reference.md#naturalhistory) of a infectious disease show us that its development has a regularity from stage to stage. The time periods from a infectious disease inform about the timing of transmission and interventions. +The [natural history](../learners/reference.md#naturalhistory) of a infectious disease shows that its development has a regularity from stage to stage. The time periods from a infectious disease inform about the timing of transmission and interventions. ![Definition of key time periods. From [Xiang et al, 2021](https://www.sciencedirect.com/science/article/pii/S2468042721000038)](fig/time-periods.jpg) @@ -56,11 +56,11 @@ The [natural history](../learners/reference.md#naturalhistory) of a infectious d ### Definitions -Take a look at the [glossary](../learners/reference.md) for all the definitions of the time periods of the figure above! +Take a look at the [glossary](../learners/reference.md) for the definitions of all the time periods of the figure above! ::::::::::::::::::::::::: -However, early in an epidemic, modelling efforts can be delayed by the lack of a centralized resource summarizing input parameters for the disease of interest ([Nash et al., 2023](https://mrc-ide.github.io/epireview/)). Projects like `{epiparameter}` and `{epireview}` are building online catalogs that can help building models faster for coming outbreaks and epidemics from known and unknown pathogens. +However, early in an epidemic, modelling efforts can be delayed by the lack of a centralized resource that summarized input parameters for the disease of interest ([Nash et al., 2023](https://mrc-ide.github.io/epireview/)). Projects like `{epiparameter}` and `{epireview}` are building online catalogs that can help building models faster for coming outbreaks and epidemics from known and unknown pathogens related to known families of viruses. @@ -75,25 +75,32 @@ library(tidyverse) ## Find a generation time -The generation time, jointly with the R0, can inform about the speed of spread and its feasibility of control. Given a $R>1$, with a shorter generation time, cases can appear more quickly. +The generation time, jointly with the $R$, can inform about the speed of spread and its feasibility of control. Given a $R>1$, with a shorter generation time, cases can appear more quickly. ![From the video by MRC Centre for Global Infectious Disease Analysis, Ep 76. Science In Context - Epi Parameter Review Group with Dr Anne Cori (27-07-2023) at ](fig/reproduction-generation-time.png) +In calculating the effective reproductive number ($Rt$), the generation time distribution is often approximated by the [serial interval](../learners/reference.md#serialinterval) distribution. +This is because the date of onset of symptoms is more easily observed than the date of onset of infectiousness. + + + +However, this is valid for diseases in which infectiousness starts after symptom onset [(Chung Lau et al. 2021)](https://academic.oup.com/jid/article/224/10/1664/6356465). In cases where infectiousness starts before symptom onset serial intervals can be negative! This is the case of a pre-symptomatic transmission. + +Even if the generation time and serial interval have the same mean, their variance usually differ propagating bias to the Rt estimation. Rt estimates are sensitive not only to the mean generation time but also to the variance and form of the generation interval distribution [(Gostic et al., 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409). + -In calculating the effective reproductive number ($Rt$), the generation time distribution is often approximated by the [serial interval](../learners/reference.md#serialinterval) distribution although this can produced biased estimates. ([Knight et al., 2020](https://www.sciencedirect.com/science/article/pii/S2468042720300634?via%3Dihub)) - ::::::::::::::::::::::::::::::::: challenge ### REVIEW -Let's assume that COVID-19 and SARS has similar Reproduction number values. +Let's assume that COVID-19 and SARS has similar Reproduction number values, and the Serial interval a good proxy of the generation interval. -Given the Serial Interval of both infections in the figure below: +Given the Serial interval of both infections in the figure below: - Which one would be harder to control? - Why do you conclude that? @@ -118,17 +125,16 @@ Which one would be harder to control? Why do you conclude that? -- COVID-19 has the lowest mean serial interval. The approximate mean value for the serial interval of COVID-19 is around 4 days, and SARS is around 7 days. Thus, COVID-19 is likely to have newer generations in less time than SARS. - +- COVID-19 has the lowest mean serial interval. The approximate mean value for the serial interval of COVID-19 is around 4 days, and SARS is around 7 days. Thus, COVID-19 is likely to have newer generations in less time than SARS, assuming similar reproduction number in both. :::::::::::::::::::::::::: ::::::::::::::::::::::::::::::::::::::::::: -Now, instead of using `EpiNow2::dist_spec()` to plug-in numeric values to specify the distribution: +Now, instead of using `EpiNow2::dist_spec()` to plug-in numeric values to specify the distribution as in the chunk below: -```{r} +```r generation_time <- dist_spec( mean = 3.6, sd = 3.1, @@ -137,7 +143,7 @@ generation_time <- dist_spec( ) ``` -Assuming that the disease we are dealing with is COVID, We can replace this code chunk and extract the Serial Interval for COVID-19 using the `epiparameter::epidist_db()` function. +Let's replace it with the Serial Interval output for COVID-19 using the `epiparameter::epidist_db()` function. (Assuming that the disease we are dealing with is COVID-19) ```{r} epiparameter::epidist_db( @@ -146,7 +152,7 @@ epiparameter::epidist_db( author = "Nishiura") ``` -We have more than one epidemiological delay. To summarize this view and get the column names from the underlying parameter dataset we connect the previous code chunk with `epiparameter::list_distributions()`: +We have more than one epidemiological delay. To summarize this view and get the column names from the underlying parameter dataset we can connect the previous code chunk using the pipe `%>%` and the `epiparameter::list_distributions()` function: ```{r} epiparameter::epidist_db( @@ -374,6 +380,12 @@ ref: https://www.reconlearn.org/post/real-time-response-2 ref: https://epiverse-trace.github.io/epimodelac/EnfermedadX.html +Recall that "Methods for accurate estimation of the generation interval from contact tracing data involve adjusting for right truncation and accounting for population susceptibility at the times transmission pairs are observed" [(Gostic et al., 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409) + +## Convert from statistical summaries to distribution parameters + +... + ## concept map update it from last epiparameter test From a21a2be075bf470fd29afbd62eba4576a74ade1e Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sun, 10 Dec 2023 21:41:10 -0500 Subject: [PATCH 061/130] add content about growth rate curve --- episodes/describe-cases.Rmd | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/episodes/describe-cases.Rmd b/episodes/describe-cases.Rmd index 40448990..f56c99d6 100644 --- a/episodes/describe-cases.Rmd +++ b/episodes/describe-cases.Rmd @@ -14,7 +14,7 @@ exercises: 2 ::::::::::::::::::::::::::::::::::::: objectives - Create incidence curves -- Estimate serial interval distribution from contact data +- Estimate the growth rate from incidence curves :::::::::::::::::::::::::::::::::::::::::::::::: @@ -22,6 +22,10 @@ exercises: 2 This is a lesson created via The Carpentries Workbench. +## Exponential growth + +Within the renewal equation, the generation interval mechanistically links the reproductive number R to observables such as the epidemic growth rate r or the number of new infections per day [(Wallinga et al. 2006)](https://royalsocietypublishing.org/doi/10.1098/rspb.2006.3754) + ::::::::::::::::::::::::::::::::::::: keypoints - Use `{incidence2}` From 1feba741d4492bd58773962d68661cf30ebf300a Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Mon, 11 Dec 2023 07:35:39 -0500 Subject: [PATCH 062/130] write text for epiparameter epidist_db --- episodes/read-delays.Rmd | 57 ++++++++++++++++++++++++++++++++++++++-- 1 file changed, 55 insertions(+), 2 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 07c34e56..da9b3c41 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -132,7 +132,23 @@ Why do you conclude that? ::::::::::::::::::::::::::::::::::::::::::: -Now, instead of using `EpiNow2::dist_spec()` to plug-in numeric values to specify the distribution as in the chunk below: +Our goal now is to replace the `generation_time` input that we used for `EpiNow2::epinow()`. + +```r +epinow_estimates <- epinow( + # cases + reported_cases = example_confirmed[1:60], + # distribution + generation_time = generation_time_opts(generation_time), + # computation + stan = stan_opts( + cores = 4, samples = 1000, chains = 3, + control = list(adapt_delta = 0.99) + ) +) +``` + +To do this replacement, instead of plug-in numeric values to `EpiNow2::dist_spec()` to specify the distribution parameters, we are going to collect them from the library of epidemiological parameters provided by `{epiparameter}`: ```r generation_time <- dist_spec( @@ -143,6 +159,32 @@ generation_time <- dist_spec( ) ``` +First, let's assume that the data set `example_confirmed` have COVID-19 observed cases. So, we need to find a reported generation time for COVID-19 or any other useful parameter for this aim. + +From the `{epiparameter}` package, we can use the `epidist_db()` function to ask for any epidemiological distribution (`epi_dist`) with the generation time using the string `generation`: + +```{r} +epiparameter::epidist_db( + epi_dist = "generation" +) +``` + +Currently, in the library of epidemiological parameters we do not have `generation` time entry for COVID-19. We can try to look at the serial intervals for COVID-19. + +```{r} +epiparameter::epidist_db( + disease = "covid", + epi_dist = "serial" +) +``` + + +The `epiparameter::epidist_db()` function works as a filtering or subset function. + +We can use other arguments like `author`, `subset` and `single_epidist` to filter even + + + Let's replace it with the Serial Interval output for COVID-19 using the `epiparameter::epidist_db()` function. (Assuming that the disease we are dealing with is COVID-19) ```{r} @@ -152,7 +194,7 @@ epiparameter::epidist_db( author = "Nishiura") ``` -We have more than one epidemiological delay. To summarize this view and get the column names from the underlying parameter dataset we can connect the previous code chunk using the pipe `%>%` and the `epiparameter::list_distributions()` function: +We get more than one epidemiological delay. To summarize this view and get the column names from the underlying parameter dataset we can add the previous code the `epiparameter::list_distributions()` function using the pipe `%>%`: ```{r} epiparameter::epidist_db( @@ -162,6 +204,8 @@ epiparameter::epidist_db( epiparameter::list_distributions() ``` +We have + If we want to filter only one ```{r} @@ -201,6 +245,15 @@ base::plot(epinow_estimates) ## formative +```{r} +# what parameters are are available for influenza? +epidist_db(disease = "influenza") %>% + list_distributions() %>% + as_tibble() %>% + count(epi_distribution) +``` + + ```{r} # generation time for ebola # if not, find serial interval and use it as serial interval From ccd7e7c73875d570e739baab76022f3f33a4037c Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Mon, 11 Dec 2023 08:55:37 -0500 Subject: [PATCH 063/130] add text before about exploring the epidist class object --- episodes/read-delays.Rmd | 96 +++++++++++++++++++++++++++++++++++----- 1 file changed, 85 insertions(+), 11 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index da9b3c41..b84ef61e 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -132,6 +132,8 @@ Why do you conclude that? ::::::::::::::::::::::::::::::::::::::::::: +## Extract epidemiological parameters + Our goal now is to replace the `generation_time` input that we used for `EpiNow2::epinow()`. ```r @@ -148,7 +150,7 @@ epinow_estimates <- epinow( ) ``` -To do this replacement, instead of plug-in numeric values to `EpiNow2::dist_spec()` to specify the distribution parameters, we are going to collect them from the library of epidemiological parameters provided by `{epiparameter}`: +To do this replacement, instead of plug-in numeric values to `EpiNow2::dist_spec()` to manually specify the distribution parameters, we are going to collect them from the library of epidemiological parameters provided by `{epiparameter}`: ```r generation_time <- dist_spec( @@ -169,7 +171,7 @@ epiparameter::epidist_db( ) ``` -Currently, in the library of epidemiological parameters we do not have `generation` time entry for COVID-19. We can try to look at the serial intervals for COVID-19. +Currently, in the library of epidemiological parameters we do not have `generation` time entry for COVID-19. We can try to look at the `serial` intervals for COVID-19. ```{r} epiparameter::epidist_db( @@ -178,35 +180,54 @@ epiparameter::epidist_db( ) ``` +::::::::::::::::: callout -The `epiparameter::epidist_db()` function works as a filtering or subset function. +`epidist_db` is not case-sensitive. This means that you can use strings with upper and lower case indistinctly. -We can use other arguments like `author`, `subset` and `single_epidist` to filter even +::::::::::::::::::::::::: +We get more than one epidemiological delay. To summarize this view and get the column names from the underlying parameter dataset we can add the previous code the `epiparameter::list_distributions()` function using the pipe `%>%`: +```{r} +epiparameter::epidist_db( + disease = "covid", + epi_dist = "serial") %>% + epiparameter::list_distributions() +``` -Let's replace it with the Serial Interval output for COVID-19 using the `epiparameter::epidist_db()` function. (Assuming that the disease we are dealing with is COVID-19) +The `epiparameter::epidist_db()` function works as a filtering or subset function. Let's use the `author` argument to filter `Hiroshi Nishiura` parameters: ```{r} epiparameter::epidist_db( disease = "covid", epi_dist = "serial", - author = "Nishiura") + author = "Hiroshi") %>% + epiparameter::list_distributions() ``` -We get more than one epidemiological delay. To summarize this view and get the column names from the underlying parameter dataset we can add the previous code the `epiparameter::list_distributions()` function using the pipe `%>%`: +We still get more than one epidemiological parameter. We can set the `single_epidist` argument to `TRUE` to only one: ```{r} epiparameter::epidist_db( disease = "covid", epi_dist = "serial", - author = "Nishiura") %>% - epiparameter::list_distributions() + author = "Hiroshi", + single_epidist = T) ``` -We have +::::::::::::::::: callout + +### How does `single_epidist` works? + +Take a look into help documentation for `?epiparameter::epidist_db()`. -If we want to filter only one +> Note: If multiple entries match the arguments supplied and single_epidist = TRUE then the `⁠⁠ that is parameterised and has the largest sample size will be returned (see `?is_parameterised()`). If multiple entries are equal after this sorting the first entry will be returned. + +::::::::::::::::::::::::: + +Now we have our epidemiological parameter of interest! We can use it to replace the number we plug-in to `EpiNow2::dist_spec()` + +Let's do this by assigning this `epidist` class object to the `covid_lnorm` object. ```{r} covid_lnorm <- epiparameter::epidist_db( @@ -216,8 +237,54 @@ covid_lnorm <- epiparameter::epidist_db( single_epidist = T) # not desired covid_lnorm +``` +## Extract the summary statistics + +We can get the `mean` and standard deviation (`sd`) from this `epidist` we can dive into the `summary_stats` object: + +```{r} +# to get the mean covid_lnorm$summary_stats$mean + +# to get the standard deviation? +covid_lnorm$summary_stats +``` + +:::::::::: discussion + +Take a minute and explore the nested content with in the `covid_lnorm` using the `$` operator plus the `TAB` button to see them as a list: + +```r +covid_lnorm$ +``` + +Share what else you discover from this! + +:::::::::::::::::::: + +::::::::: instructor + +An interesting example is the `method_assess` nested entry, which refers to the methods used to assess for the bias while estimating the serial interval distribution. + +```{r} +covid_lnorm$method_assess +``` + +Will explore this at the end! + +:::::::::::::::::: + +`EpiNow2::dist_spec()` still needs + +The epidemiological parameter in `covid_lnorm` is a continuous distribution: + +```{r} +plot(covid_lnorm) +``` + + +```{r} covid_lnorm_discrete <- epiparameter::discretise(covid_lnorm) covid_lnorm_discrete_max <- covid_lnorm_discrete$prob_dist$q(p = 0.999) @@ -425,6 +492,13 @@ The three examples above represent transmission parameters. ## From our data! (callout) +An interesting example is the `method_assess` nested entry, which refers to the methods used to assess for the bias while estimating the serial interval distribution. + +```{r} +covid_lnorm$method_assess +``` + + Fit a parametric distribution to estimate the mean and sd estimate serial interval from case and contact data pairs From 68367c42587aed1d459c3f6c6281e5d5161aa2bb Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Mon, 11 Dec 2023 11:07:43 -0500 Subject: [PATCH 064/130] add challenge format --- episodes/read-delays.Rmd | 40 +++++++++++++++++++++++++++------------- 1 file changed, 27 insertions(+), 13 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index b84ef61e..52bfa388 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -241,39 +241,53 @@ covid_lnorm ## Extract the summary statistics -We can get the `mean` and standard deviation (`sd`) from this `epidist` we can dive into the `summary_stats` object: +We can get the `mean` and standard deviation (`sd`) from this `epidist` diving into the `summary_stats` object: ```{r} -# to get the mean +# get the mean covid_lnorm$summary_stats$mean - -# to get the standard deviation? -covid_lnorm$summary_stats ``` -:::::::::: discussion +:::::::::::::::::::::::::::::: challenge + +## How to get the `sd` and other nested elements? + +Take 1 minute to: + +- Get the `sd` from the `epidist` object. -Take a minute and explore the nested content with in the `covid_lnorm` using the `$` operator plus the `TAB` button to see them as a list: +- Explore all the other nested elements within the `epidist` object. + +Share what is the most interesting element for you! + +::::::::: hint + +Using the `$` operator plus the `TAB` keyboard button to explore them as an expandable list: ```r covid_lnorm$ ``` -Share what else you discover from this! +:::::::::::::::::: -:::::::::::::::::::: +:::::::::: solution -::::::::: instructor +```{r} +# get the sd +covid_lnorm$summary_stats$sd +``` -An interesting example is the `method_assess` nested entry, which refers to the methods used to assess for the bias while estimating the serial interval distribution. +An interesting element is the `method_assess` nested entry, which refers to the methods used by the study authora to assess for the bias while estimating the serial interval distribution. ```{r} covid_lnorm$method_assess ``` -Will explore this at the end! +We will explore this at the end! -:::::::::::::::::: +:::::::::::::::::::: + +:::::::::::::::::::::::::::::::: `EpiNow2::dist_spec()` still needs From cd79f59440194eb0ad86c1d6bb14cd44921559b7 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Mon, 11 Dec 2023 16:36:48 +0000 Subject: [PATCH 065/130] complete with the max argument for dist_spec --- episodes/read-delays.Rmd | 34 ++++++++++++++++++++++++++++++++-- 1 file changed, 32 insertions(+), 2 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 52bfa388..57a7accb 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -289,19 +289,49 @@ We will explore this at the end! :::::::::::::::::::::::::::::::: -`EpiNow2::dist_spec()` still needs +## Discretize a continuous distribution -The epidemiological parameter in `covid_lnorm` is a continuous distribution: +We are almost halfway! `EpiNow2::dist_spec()` still needs a maximum value (`max`). + +One way to do this is getting the quantile value for the the 99.9% or `0.999` cumulative probability of the distribution. For this we need to access to the set of distribution functions for our `epidist` object. + +To achieve this we can **discretize** the continuous distribution stored in our `epidist` object. + +The epidemiological parameter stored in `covid_lnorm` is a **continuous** distribution. Here we plot its *Probability Density Function (PDF)*: ```{r} plot(covid_lnorm) ``` +When we `epiparameter::discretise()` the continuous distribution we get a **discrete** distribution: ```{r} covid_lnorm_discrete <- epiparameter::discretise(covid_lnorm) + +covid_lnorm_discrete +``` + +We identify this change in the `Distribution:` output line of the `epidist` object. Take a double check to this line: + +``` +Distribution: discrete lnorm +``` + +For a **discrete** distribution, we plot the *Probability Mass Function (PMF)*: + +```{r} +plot(covid_lnorm_discrete) +``` + +To finally get a `max` value let's access to the quantile value of the 99% or `0.999` probability of the distribution with the `prob_dist$q` notation, similarly to how we access to the `summary_stats` values. + +```{r} covid_lnorm_discrete_max <- covid_lnorm_discrete$prob_dist$q(p = 0.999) +``` +Now we are ready to plug-in all to the `EpiNow2::dist_spec()` function! + +```{r} generation_time_epiparam <- dist_spec( mean = covid_lnorm$summary_stats$mean, sd = covid_lnorm$summary_stats$sd, From 2e1d1c0bef5218fe1b5df34e0449f3113263f850 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Mon, 11 Dec 2023 21:08:05 +0000 Subject: [PATCH 066/130] add spoiler on distribution plus explore output --- episodes/read-delays.Rmd | 75 ++++++++++++++++++++++++++++++++++++---- 1 file changed, 68 insertions(+), 7 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 57a7accb..3909e123 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -289,6 +289,31 @@ We will explore this at the end! :::::::::::::::::::::::::::::::: +::::::::::::::::: spoiler + +### The distribution Zoo + +How to read this notation? + +```{r} +covid_lnorm$prob_dist +``` + +This object is class `distribution`. + +```{r} +covid_lnorm$prob_dist %>% class() +``` + +You can look further into it `?distributional::dist_lognormal()` or the corresponding distribution that you need. + +We also recommend your this shinyapp called **The Distribution Zoo**! + + + +::::::::::::::::::::::::: + + ## Discretize a continuous distribution We are almost halfway! `EpiNow2::dist_spec()` still needs a maximum value (`max`). @@ -329,16 +354,52 @@ To finally get a `max` value let's access to the quantile value of the 99% or `0 covid_lnorm_discrete_max <- covid_lnorm_discrete$prob_dist$q(p = 0.999) ``` +::::::::::::::::::::::::::::::::: challenge + +### DISTRIBUTION FUNCTIONS + +TODO epxlore epiparameter internal dsitrubtion functions for discrete distrib + +::::::::::::::::: hint + +### HINT + +HINT accordions are created with the `hint` tag and nested within +`challenge` callouts. These offer more context for the challenge that can help +the learner find the solution. + +:::::::::::::::::::::: + +::::::::::::::::: solution + +### SOLUTION + +SOLUTION accordions are created with the `solution` tag and nested within +`challenge` callouts. These provide the learners with the solution to the +challenge so they can confirm their answers. + +:::::::::::::::::::::::::: + + +::::::::::::::::::::::::::::::::::::::::::: + Now we are ready to plug-in all to the `EpiNow2::dist_spec()` function! ```{r} -generation_time_epiparam <- dist_spec( - mean = covid_lnorm$summary_stats$mean, - sd = covid_lnorm$summary_stats$sd, - max = covid_lnorm_discrete_max, - distribution = "lognormal" -) +generation_time_epiparam <- + dist_spec( + mean = covid_lnorm$summary_stats$mean, + sd = covid_lnorm$summary_stats$sd, + max = covid_lnorm_discrete_max, + distribution = "lognormal" + ) +generation_time_epiparam +``` + +Now, let's replace the `generation_time` input that we used for `EpiNow2::epinow()`. + +```{r} epinow_estimates <- epinow( # cases reported_cases = example_confirmed[1:60], @@ -354,7 +415,7 @@ epinow_estimates <- epinow( base::plot(epinow_estimates) ``` -## formative +### formative ```{r} # what parameters are are available for influenza? From ff72a76891531e2979dfbbbc1435dabab9a7caa7 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Tue, 12 Dec 2023 22:06:15 +0000 Subject: [PATCH 067/130] add offspring distribution to glossary --- learners/reference.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/learners/reference.md b/learners/reference.md index 79bd9c3d..18718283 100644 --- a/learners/reference.md +++ b/learners/reference.md @@ -95,7 +95,10 @@ This can be different to the [incubation period](#incubation) as shown in Figure [Natural history of disease]{#naturalhistory} : Refers to the development of disease from beginning to end without any treatment or intervention. In fact, given the harmfulness of an epidemic, treatment or intervention measures are inevitable. Therefore, it is difficult for the natural history of a disease to be unaffected by the various coupling factors. ([Xiang et al, 2021](https://www.sciencedirect.com/science/article/pii/S2468042721000038)) - +## O + +[Offspring distribution]{#offspringdist} +: Distribution of the number of secondary cases caused by a particular infected individual. ([Lloyd-Smith et al., 2005](https://www.nature.com/articles/nature04153), [Endo et al., 2020](https://wellcomeopenresearch.org/articles/5-67/v3)) From 7a6ca59f63c76579c1b9242670ba7c4eaf5a580a Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Tue, 12 Dec 2023 22:06:48 +0000 Subject: [PATCH 068/130] add formative assessments on distributions --- episodes/read-delays.Rmd | 271 +++++++++++++++++++++++++++++++++------ 1 file changed, 232 insertions(+), 39 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 3909e123..ce5ea3a7 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -96,7 +96,7 @@ Even if the generation time and serial interval have the same mean, their varian ::::::::::::::::::::::::::::::::: challenge -### REVIEW +### Serial interval Let's assume that COVID-19 and SARS has similar Reproduction number values, and the Serial interval a good proxy of the generation interval. @@ -109,16 +109,12 @@ Given the Serial interval of both infections in the figure below: ::::::::::::::::: hint -### HINT - The peak of each curve can inform you about the location of the mean of each distribution. The larger the mean, the larger the serial interval. :::::::::::::::::::::: ::::::::::::::::: solution -### SOLUTION - Which one would be harder to control? - COVID-19 @@ -161,9 +157,19 @@ generation_time <- dist_spec( ) ``` -First, let's assume that the data set `example_confirmed` have COVID-19 observed cases. So, we need to find a reported generation time for COVID-19 or any other useful parameter for this aim. +First, let's assume that the data set `example_confirmed` have COVID-19 observed cases. So, we need to find a reported generation time for COVID-19 or any other useful parameter for this aim. + +Let's start by looking at how many parameters do we have in the epidemiological distributions database (`epidist_db`) for the `disease` named `covid`-19: + +```{r} +epiparameter::epidist_db( + disease = "covid" +) +``` + +From the `{epiparameter}` package, we can use the `epidist_db()` function to ask for any `disease` and also for an specific epidemiological distribution (`epi_dist`). -From the `{epiparameter}` package, we can use the `epidist_db()` function to ask for any epidemiological distribution (`epi_dist`) with the generation time using the string `generation`: +Let's ask now how many parameters do we have in the epidemiological distributions database (`epidist_db`) with the generation time using the string `generation`: ```{r} epiparameter::epidist_db( @@ -171,18 +177,18 @@ epiparameter::epidist_db( ) ``` -Currently, in the library of epidemiological parameters we do not have `generation` time entry for COVID-19. We can try to look at the `serial` intervals for COVID-19. +Currently, in the library of epidemiological parameters we have one `generation` time entry but for Influenza. We can try to look at the `serial` intervals for `COVID`-19. ```{r} epiparameter::epidist_db( - disease = "covid", + disease = "COVID", epi_dist = "serial" ) ``` ::::::::::::::::: callout -`epidist_db` is not case-sensitive. This means that you can use strings with upper and lower case indistinctly. +`epidist_db` is [case-insensitive](https://dillionmegida.com/p/case-sensitivity-vs-case-insensitivity/#case-insensitivity). This means that you can use strings with letters in upper or lower case indistinctly. ::::::::::::::::::::::::: @@ -191,7 +197,8 @@ We get more than one epidemiological delay. To summarize this view and get the c ```{r} epiparameter::epidist_db( disease = "covid", - epi_dist = "serial") %>% + epi_dist = "serial" +) %>% epiparameter::list_distributions() ``` @@ -201,7 +208,8 @@ The `epiparameter::epidist_db()` function works as a filtering or subset functio epiparameter::epidist_db( disease = "covid", epi_dist = "serial", - author = "Hiroshi") %>% + author = "Hiroshi" +) %>% epiparameter::list_distributions() ``` @@ -212,7 +220,8 @@ epiparameter::epidist_db( disease = "covid", epi_dist = "serial", author = "Hiroshi", - single_epidist = T) + single_epidist = T +) ``` ::::::::::::::::: callout @@ -221,24 +230,77 @@ epiparameter::epidist_db( Take a look into help documentation for `?epiparameter::epidist_db()`. -> Note: If multiple entries match the arguments supplied and single_epidist = TRUE then the `⁠⁠ that is parameterised and has the largest sample size will be returned (see `?is_parameterised()`). If multiple entries are equal after this sorting the first entry will be returned. +> Note: If multiple entries match the arguments supplied and `single_epidist = TRUE` then the `⁠⁠ that is parameterised and has the largest sample size will be returned (see `?is_parameterised()`). If multiple entries are equal after this sorting the first entry will be returned. ::::::::::::::::::::::::: -Now we have our epidemiological parameter of interest! We can use it to replace the number we plug-in to `EpiNow2::dist_spec()` +Now we have an epidemiological parameter we can reuse! We can use it to replace the numbers we plug-in to `EpiNow2::dist_spec()`. -Let's do this by assigning this `epidist` class object to the `covid_lnorm` object. +Let's do this by assigning this `epidist` class object to the `covid_lnorm` object, where `lnorm` means that this epiparameter follows a log-normal distribution. ```{r} covid_lnorm <- epiparameter::epidist_db( disease = "covid", epi_dist = "serial", author = "Nishiura", - single_epidist = T) # not desired + single_epidist = T +) covid_lnorm ``` +::::::::::::::::::::::::::::::::: challenge + +### Ebola's incubation period + + + +- How many delay distributions are for the Ebola disease? + +- How many delay distributions are for the incubation period of Ebola? + +- What type of distribution has the incubation period of Ebola with highest sample size? + +::::::::::::::::: hint + +The `{epiparameter}` combo of `epidist_db()` plus `list_distributions()` list all the entries by: +- disease, +- epidemiological distribution, +- the type of the probability distribution, +- author of the study, and +- year of study. + +:::::::::::::::::::::: + +::::::::::::::::: solution + +```{r} +# 16 delays distributions +epiparameter::epidist_db( + disease = "ebola" +) + +# 5 delay distributions are for the incubation period +epiparameter::epidist_db( + disease = "ebola", + epi_dist = "incubation" +) + +# the distribution with the highest sample size has a gamma distribution +epiparameter::epidist_db( + disease = "ebola", + epi_dist = "incubation", + single_epidist = T +) +``` + +Now, from the output of `epiparameter::epidist_db()`, What is an [offspring distribution](../learners/reference.md#offspringdist)? + +:::::::::::::::::::::::::: + + +::::::::::::::::::::::::::::::::::::::::::: + ## Extract the summary statistics We can get the `mean` and standard deviation (`sd`) from this `epidist` diving into the `summary_stats` object: @@ -250,24 +312,28 @@ covid_lnorm$summary_stats$mean :::::::::::::::::::::::::::::: challenge -## How to get the `sd` and other nested elements? +### How to get the `sd` and other nested elements? Take 1 minute to: -- Get the `sd` from the `epidist` object. +1. Get the `sd` of the epidemiological distribution. + +2. Find the `sample_size` used in the study. -- Explore all the other nested elements within the `epidist` object. +3. Explore all the other nested elements within the `epidist` object. Share what is the most interesting element for you! ::::::::: hint -Using the `$` operator plus the `TAB` keyboard button to explore them as an expandable list: +Use the `$` operator plus the tab keyboard button to explore them as an expandable list: ```r covid_lnorm$ ``` +Use the `str()` to display the structure of the `epidist` R object. + :::::::::::::::::: :::::::::: solution @@ -275,15 +341,18 @@ covid_lnorm$ ```{r} # get the sd covid_lnorm$summary_stats$sd + +# get the sample_size +covid_lnorm$metadata$sample_size ``` -An interesting element is the `method_assess` nested entry, which refers to the methods used by the study authora to assess for the bias while estimating the serial interval distribution. +An interesting element is the `method_assess` nested entry, which refers to the methods used by the study authors to assess for the bias while estimating the serial interval distribution. ```{r} covid_lnorm$method_assess ``` -We will explore this at the end! +We will explore these concepts at the end! :::::::::::::::::::: @@ -311,16 +380,61 @@ We also recommend your this shinyapp called **The Distribution Zoo**! +You can go to the left panel and the second option named `Distribution type`. Select `Log-Normal` and the name of the [sliders](https://shiny.posit.co/r/articles/build/sliders/) will change according to the distribution of your interest. + ::::::::::::::::::::::::: +## Distribution functions + +TODO + +... + +::::::::::::::::::::::::::::::::: challenge + +### 99% of serial interval? + +When considering secondary cases, after how many days following the symptom onset of primary cases can we expect 99% of symptom onset to occur? + +::::::::::::::::: hint + +```{r} +# the probability density function and +# the cumulative distribution function of the distribution +plot(covid_lnorm,day_range = 0:20) +# the density value at quantile value of 10 +stats::density(covid_lnorm,at = 10) +# the cumulative probability at quantile value of 10 +epiparameter::cdf(covid_lnorm,q = 10) +# the quantile value at a cumulative probability of 50% +stats::quantile(covid_lnorm,p = 0.5) +# generate 10 random values given the distribution family and its parameters +epiparameter::generate(covid_lnorm,times = 10) +``` + +:::::::::::::::::::::: + +::::::::::::::::: solution + +```{r} +stats::quantile(covid_lnorm,p = 0.99) +``` + +The 99% percent of the symptom onset of secondary cases will happen after 14.99 days after the symptom onset of primary cases. + +:::::::::::::::::::::::::: + + +::::::::::::::::::::::::::::::::::::::::::: + ## Discretize a continuous distribution We are almost halfway! `EpiNow2::dist_spec()` still needs a maximum value (`max`). -One way to do this is getting the quantile value for the the 99.9% or `0.999` cumulative probability of the distribution. For this we need to access to the set of distribution functions for our `epidist` object. +One way to do this is getting the quantile value for the the 99th percentile or `0.99` cumulative probability of the distribution. For this we need to access to the set of distribution functions for our `epidist` object. -To achieve this we can **discretize** the continuous distribution stored in our `epidist` object. +We can use the set of [distribution functions](https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html#distribution-functions) for a continuous distribution. However, this values will be _continuous_ numbers. To get discrete values from a continuous distribution we can **discretize** the continuous distribution stored in our `epidist` object. The epidemiological parameter stored in `covid_lnorm` is a **continuous** distribution. Here we plot its *Probability Density Function (PDF)*: @@ -348,35 +462,60 @@ For a **discrete** distribution, we plot the *Probability Mass Function (PMF)*: plot(covid_lnorm_discrete) ``` -To finally get a `max` value let's access to the quantile value of the 99% or `0.999` probability of the distribution with the `prob_dist$q` notation, similarly to how we access to the `summary_stats` values. +To finally get a `max` value let's access to the quantile value of the 99th percentile or `0.99` probability of the distribution with the `prob_dist$q` notation, similarly to how we access to the `summary_stats` values. ```{r} -covid_lnorm_discrete_max <- covid_lnorm_discrete$prob_dist$q(p = 0.999) +covid_lnorm_discrete_max <- covid_lnorm_discrete$prob_dist$q(p = 0.99) ``` ::::::::::::::::::::::::::::::::: challenge -### DISTRIBUTION FUNCTIONS +### 99% of incubation period -TODO epxlore epiparameter internal dsitrubtion functions for discrete distrib +Within what time frame do 99% of individuals who develop symptoms exhibit them after infection? ::::::::::::::::: hint -### HINT +```{r} +plot(covid_lnorm_discrete,day_range = 0:20) +# generate random values +covid_lnorm_discrete$prob_dist$r(1000) +# in what quantile we have the 99.9% cumulative probability? +covid_lnorm_discrete$prob_dist$q(0.99) +# covid_lnorm_discrete$prob_dist$qf(0.99) # as in continuous distribution? +x_limit <- covid_lnorm_discrete$prob_dist$q(0.99) +# generate quantile values as a sequence for each natural number +si_discrete_x <- seq(1L, to = x_limit, by = 1L) +# calculate the values for each quantiles in the density function +si_discrete_y <- covid_lnorm_discrete$prob_dist$d(si_discrete_x) + +tibble( + quantile_values = si_discrete_x, + density_values = si_discrete_y +) %>% + ggplot(aes(quantile_values,density_values)) + + geom_col() +``` -HINT accordions are created with the `hint` tag and nested within -`challenge` callouts. These offer more context for the challenge that can help -the learner find the solution. :::::::::::::::::::::: ::::::::::::::::: solution -### SOLUTION +```{r} +covid_incubation <- + epiparameter::epidist_db( + disease = "covid", + epi_dist = "incubation", + single_epidist = T + ) + +covid_incubation_discrete <- epiparameter::discretise(covid_incubation) + +covid_incubation_discrete$prob_dist$q(0.99) +``` -SOLUTION accordions are created with the `solution` tag and nested within -`challenge` callouts. These provide the learners with the solution to the -challenge so they can confirm their answers. +99% of those who develop symptoms will do so within 16 days of infection. :::::::::::::::::::::::::: @@ -431,7 +570,7 @@ epidist_db(disease = "influenza") %>% # if not, find serial interval and use it as serial interval ``` -## Find the incubation time +## Find the incubation period What it is? With which other is related? @@ -441,6 +580,49 @@ The natural history of a disease influence the transmission dynamics. For `{EpiN ![The relationship between the incubation period and serial interval. From [Nishiura 2020](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext)](fig/incubation-period-serial-interval.jpg) ![Parameter estimates. Plausible ranges for the key parameters R0 and θ (see main text for sources) for four viral infections of public concern are shown as shaded regions. The size of the shaded area reflects the uncertainties in the parameter estimates. [Fraser et al., 2004](https://www.pnas.org/doi/10.1073/pnas.0307506101)](fig/reproduction-number-pre-symptomatic.png) + + +::::::::::::::::::::::::::::::::: challenge + +### WHAT TYPE OF TRANSMISSION? + +... + +::::::::::::::::: hint + +... + +:::::::::::::::::::::: + +::::::::::::::::: solution + +```{r} +# pre-symptomatic transmission +epidist_db(disease = "influenza",epi_dist = "incubation",single_epidist = T)$summary_stats$mean +epidist_db(disease = "influenza",epi_dist = "serial",single_epidist = T)$summary_stats$mean + +# symptomatic transmission +epidist_db(disease = "mers",epi_dist = "incubation",single_epidist = T)$summary_stats$median +epidist_db(disease = "mers",epi_dist = "serial",single_epidist = T)$summary_stats$mean +``` + +```{r} +# pre-symptomatic transmission +epidist_db(disease = "covid",epi_dist = "incubation",author = "Stephen",single_epidist = T)$summary_stats$mean +epidist_db(disease = "covid",epi_dist = "serial",author = "Nishiura",single_epidist = T)$summary_stats$mean + +# symptomatic transmission +epidist_db(disease = "ebola",epi_dist = "incubation",single_epidist = T)$summary_stats$mean +epidist_db(disease = "ebola",epi_dist = "serial",single_epidist = T)$summary_stats$mean +``` + + +:::::::::::::::::::::::::: + + +::::::::::::::::::::::::::::::::::::::::::: + + @@ -553,6 +735,17 @@ The natural history of a disease influence the transmission dynamics. For `{EpiN ![Timeline for chain of disease reporting, the Netherlands. Lab, laboratory; PHA, public health authority. From [Marinović et al., 2015](https://wwwnc.cdc.gov/eid/article/21/2/13-0504_article)](fig/disease-reporting.jpg) + +## any epiparameter? + +```{r} +epidist_db(disease = "all") %>% list_distributions() %>% + as_tibble() %>% + distinct(epi_distribution) +``` + +## replace in epinow2 + ```{r} reporting_delay <- dist_spec( mean = convert_to_logmean(2, 1), @@ -616,7 +809,7 @@ Recall that "Methods for accurate estimation of the generation interval from con ## Convert from statistical summaries to distribution parameters -... +https://github.com/epiverse-trace/epiparameter/issues/114 ## concept map From 878c26ad68f26205fb22f759c96ca2c88c65694b Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 14 Dec 2023 03:19:06 +0000 Subject: [PATCH 069/130] add text and assessment about continuous distribution functions --- episodes/read-delays.Rmd | 225 +++++++++++++++++++++++++++------------ 1 file changed, 158 insertions(+), 67 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index ce5ea3a7..0b031532 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -188,6 +188,8 @@ epiparameter::epidist_db( ::::::::::::::::: callout +### CASE-INSENSITIVE + `epidist_db` is [case-insensitive](https://dillionmegida.com/p/case-sensitivity-vs-case-insensitivity/#case-insensitivity). This means that you can use strings with letters in upper or lower case indistinctly. ::::::::::::::::::::::::: @@ -202,6 +204,8 @@ epiparameter::epidist_db( epiparameter::list_distributions() ``` +## Select a single distribution + The `epiparameter::epidist_db()` function works as a filtering or subset function. Let's use the `author` argument to filter `Hiroshi Nishiura` parameters: ```{r} @@ -230,30 +234,31 @@ epiparameter::epidist_db( Take a look into help documentation for `?epiparameter::epidist_db()`. -> Note: If multiple entries match the arguments supplied and `single_epidist = TRUE` then the `⁠⁠ that is parameterised and has the largest sample size will be returned (see `?is_parameterised()`). If multiple entries are equal after this sorting the first entry will be returned. +> Note: If multiple entries match the arguments supplied and `single_epidist = TRUE` then the `⁠⁠ that is parameterised and has the largest sample size will be returned (read `?is_parameterised()`). If multiple entries are equal after this sorting the first entry will be returned. ::::::::::::::::::::::::: Now we have an epidemiological parameter we can reuse! We can use it to replace the numbers we plug-in to `EpiNow2::dist_spec()`. -Let's do this by assigning this `epidist` class object to the `covid_lnorm` object, where `lnorm` means that this epiparameter follows a log-normal distribution. +Let's do this by assigning this `` class object to the `covid_serialint` object. ```{r} -covid_lnorm <- epiparameter::epidist_db( - disease = "covid", - epi_dist = "serial", - author = "Nishiura", - single_epidist = T -) +covid_serialint <- + epiparameter::epidist_db( + disease = "covid", + epi_dist = "serial", + author = "Nishiura", + single_epidist = T + ) -covid_lnorm +covid_serialint ``` ::::::::::::::::::::::::::::::::: challenge ### Ebola's incubation period - +Take 5 minutes: - How many delay distributions are for the Ebola disease? @@ -303,11 +308,11 @@ Now, from the output of `epiparameter::epidist_db()`, What is an [offspring dist ## Extract the summary statistics -We can get the `mean` and standard deviation (`sd`) from this `epidist` diving into the `summary_stats` object: +We can get the `mean` and standard deviation (`sd`) from this `` diving into the `summary_stats` object: ```{r} # get the mean -covid_lnorm$summary_stats$mean +covid_serialint$summary_stats$mean ``` :::::::::::::::::::::::::::::: challenge @@ -320,7 +325,7 @@ Take 1 minute to: 2. Find the `sample_size` used in the study. -3. Explore all the other nested elements within the `epidist` object. +3. Explore all the other nested elements within the `` object. Share what is the most interesting element for you! @@ -329,10 +334,10 @@ Share what is the most interesting element for you! Use the `$` operator plus the tab keyboard button to explore them as an expandable list: ```r -covid_lnorm$ +covid_serialint$ ``` -Use the `str()` to display the structure of the `epidist` R object. +Use the `str()` to display the structure of the `` R object. :::::::::::::::::: @@ -340,16 +345,16 @@ Use the `str()` to display the structure of the `epidist` R object. ```{r} # get the sd -covid_lnorm$summary_stats$sd +covid_serialint$summary_stats$sd # get the sample_size -covid_lnorm$metadata$sample_size +covid_serialint$metadata$sample_size ``` An interesting element is the `method_assess` nested entry, which refers to the methods used by the study authors to assess for the bias while estimating the serial interval distribution. ```{r} -covid_lnorm$method_assess +covid_serialint$method_assess ``` We will explore these concepts at the end! @@ -358,70 +363,156 @@ We will explore these concepts at the end! :::::::::::::::::::::::::::::::: -::::::::::::::::: spoiler +## Continuous distributions -### The distribution Zoo +The following output has four entries with different content in the probability distribution (`prob_distribution`) column: + +```{r} +distribution <- + epiparameter::epidist_db( + disease = "covid", + epi_dist = "serial" + ) + +distribution %>% + list_distributions() +``` -How to read this notation? +Entries with a missing value (``) in the `prob_distribution` column only posses summary statistics associated with it. Run these commands to find the difference: + +```{r,eval=FALSE} +distribution[[1]]$summary_stats +distribution[[1]]$prob_dist +``` + +As detailed in `?is_parameterised`, a parameterised distribution is the entry that has a probability distribution associated to it provided by an `inference_method` as shown in `metadata`. Run these commands to find the difference: + +```{r,eval=FALSE} +distribution[[1]]$metadata$inference_method +distribution[[2]]$metadata$inference_method +distribution[[3]]$metadata$inference_method +distribution[[4]]$metadata$inference_method +``` + +In the `epiparameter::list_distributions()` output, we can also find different types of probability distributions (e.g., log-normal, weibull, normal). ```{r} -covid_lnorm$prob_dist +distribution %>% + list_distributions() ``` -This object is class `distribution`. +In `{epiparameter}` you will mostly find **continuous** distributions like these. You can visualize any of them with the `plot()` function and access to: + +- the *Probability Density Function (PDF)* and +- the *Cumulative Distribution Function (CDF)*. ```{r} -covid_lnorm$prob_dist %>% class() +plot(distribution[[2]], day_range = 0:20) ``` -You can look further into it `?distributional::dist_lognormal()` or the corresponding distribution that you need. +With the `day_range` argument you can change the length or number of days in the `x` axis. + +::::::::::::::::: discussion + +### The distribution Zoo + +Explore this shinyapp called **The Distribution Zoo**! -We also recommend your this shinyapp called **The Distribution Zoo**! +Follow these steps to reproduce the `covid_serialint` distribution: - +- Access to shinyapp website! +- Go to the left panel, +- Keep the *Category of distribution*: `Continuous Univariate`, +- Select a new *Distribution type*: `Log-Normal`, +- Move the **sliders**, i.e. the graphical control element that allows you to adjust a value by moving a handle along a track or bar, to the `covid_serialint` parameters. -You can go to the left panel and the second option named `Distribution type`. Select `Log-Normal` and the name of the [sliders](https://shiny.posit.co/r/articles/build/sliders/) will change according to the distribution of your interest. + + +Replicate these with the `distribution` object and all its list elements: `2`, `3`, and `4`. Explore: + +- How does the shape of a distribution change when its parameters change? + +Share what other useful features you find in the website. ::::::::::::::::::::::::: ## Distribution functions -TODO +In R, all the statistical distributions has functions to access to: -... +- density, +- cumulative distribution function, +- quantile function, and +- random variate generation. + +:::::::::::: spoiler + +### Functions for the Normal distribution + +If you needed it, read in detail about the [R probability functions for the normal distribution](https://sakai.unc.edu/access/content/group/3d1eb92e-7848-4f55-90c3-7c72a54e7e43/public/docs/lectures/lecture13.htm#probfunc), each of its definitions and identify in which part of a distribution they are located! + +![The four probability functions for the normal distribution ([Jack Weiss, 2012](https://sakai.unc.edu/access/content/group/3d1eb92e-7848-4f55-90c3-7c72a54e7e43/public/docs/lectures/lecture13.htm#probfunc))](fig/fig5a-normaldistribution.png) + +:::::::::::::::::::: + +If you look at `?stats::Distributions`, each type of distribution has a unique set functions. However, `{epiparameter}` allows gives you one same set of four functions to access to each of the values above for any `` object you want! + +```{r,eval=FALSE} +# plot this to have a visual reference +plot(covid_serialint, day_range = 0:20) +``` + +```{r} +# the density value at quantile value of 10 (days) +density(covid_serialint, at = 10) + +# the cumulative probability at quantile value of 10 (days) +cdf(covid_serialint, q = 10) + +# the quantile value (day) at a cumulative probability of 60% +quantile(covid_serialint, p = 0.6) + +# generate 10 random values (days) given the distribution family and its parameters +generate(covid_serialint, times = 10) +``` + +::::::::: instructor + +Access to the reference documentation (Help files) for these functions is accessible with the three double-colon notation: `epiparameter:::` + +```{r} +?epiparameter:::density.epidist() +?epiparameter:::cdf.epidist() +?epiparameter:::quantile.epidist() +?epiparameter:::generate.epidist() +``` + +:::::::::::::::::: ::::::::::::::::::::::::::::::::: challenge -### 99% of serial interval? +### 99th percentile of the serial interval? When considering secondary cases, after how many days following the symptom onset of primary cases can we expect 99% of symptom onset to occur? ::::::::::::::::: hint -```{r} -# the probability density function and -# the cumulative distribution function of the distribution -plot(covid_lnorm,day_range = 0:20) -# the density value at quantile value of 10 -stats::density(covid_lnorm,at = 10) -# the cumulative probability at quantile value of 10 -epiparameter::cdf(covid_lnorm,q = 10) -# the quantile value at a cumulative probability of 50% -stats::quantile(covid_lnorm,p = 0.5) -# generate 10 random values given the distribution family and its parameters -epiparameter::generate(covid_lnorm,times = 10) -``` +In Figure 5 from the [R probability functions for the normal distribution](https://sakai.unc.edu/access/content/group/3d1eb92e-7848-4f55-90c3-7c72a54e7e43/public/docs/lectures/lecture13.htm#probfunc), the shadowed section represents a cumulative probability of `0.997` for the quantile value at `x = 2`. :::::::::::::::::::::: ::::::::::::::::: solution ```{r} -stats::quantile(covid_lnorm,p = 0.99) +quantile(covid_serialint, p = 0.99) ``` The 99% percent of the symptom onset of secondary cases will happen after 14.99 days after the symptom onset of primary cases. +_Does this result make sense or is expected?_ + :::::::::::::::::::::::::: @@ -432,25 +523,25 @@ The 99% percent of the symptom onset of secondary cases will happen after 14.99 We are almost halfway! `EpiNow2::dist_spec()` still needs a maximum value (`max`). -One way to do this is getting the quantile value for the the 99th percentile or `0.99` cumulative probability of the distribution. For this we need to access to the set of distribution functions for our `epidist` object. +One way to do this is getting the quantile value for the the 99th percentile or `0.99` cumulative probability of the distribution. For this we need to access to the set of distribution functions for our `` object. -We can use the set of [distribution functions](https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html#distribution-functions) for a continuous distribution. However, this values will be _continuous_ numbers. To get discrete values from a continuous distribution we can **discretize** the continuous distribution stored in our `epidist` object. +We can use the set of [distribution functions](https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html#distribution-functions) for a continuous distribution. However, this values will be _continuous_ numbers. To get discrete values from a continuous distribution we can **discretize** the continuous distribution stored in our `` object. -The epidemiological parameter stored in `covid_lnorm` is a **continuous** distribution. Here we plot its *Probability Density Function (PDF)*: +The epidemiological parameter stored in `covid_serialint` is a **continuous** distribution. Here we plot its *Probability Density Function (PDF)*: ```{r} -plot(covid_lnorm) +plot(covid_serialint) ``` When we `epiparameter::discretise()` the continuous distribution we get a **discrete** distribution: ```{r} -covid_lnorm_discrete <- epiparameter::discretise(covid_lnorm) +covid_serialint_discrete <- epiparameter::discretise(covid_serialint) -covid_lnorm_discrete +covid_serialint_discrete ``` -We identify this change in the `Distribution:` output line of the `epidist` object. Take a double check to this line: +We identify this change in the `Distribution:` output line of the `` object. Take a double check to this line: ``` Distribution: discrete lnorm @@ -459,35 +550,35 @@ Distribution: discrete lnorm For a **discrete** distribution, we plot the *Probability Mass Function (PMF)*: ```{r} -plot(covid_lnorm_discrete) +plot(covid_serialint_discrete) ``` To finally get a `max` value let's access to the quantile value of the 99th percentile or `0.99` probability of the distribution with the `prob_dist$q` notation, similarly to how we access to the `summary_stats` values. ```{r} -covid_lnorm_discrete_max <- covid_lnorm_discrete$prob_dist$q(p = 0.99) +covid_serialint_discrete_max <- covid_serialint_discrete$prob_dist$q(p = 0.99) ``` ::::::::::::::::::::::::::::::::: challenge -### 99% of incubation period +### 99th percentile of the incubation period Within what time frame do 99% of individuals who develop symptoms exhibit them after infection? ::::::::::::::::: hint ```{r} -plot(covid_lnorm_discrete,day_range = 0:20) +plot(covid_serialint_discrete,day_range = 0:20) # generate random values -covid_lnorm_discrete$prob_dist$r(1000) +covid_serialint_discrete$prob_dist$r(1000) # in what quantile we have the 99.9% cumulative probability? -covid_lnorm_discrete$prob_dist$q(0.99) -# covid_lnorm_discrete$prob_dist$qf(0.99) # as in continuous distribution? -x_limit <- covid_lnorm_discrete$prob_dist$q(0.99) +covid_serialint_discrete$prob_dist$q(0.99) +# covid_serialint_discrete$prob_dist$qf(0.99) # as in continuous distribution? +x_limit <- covid_serialint_discrete$prob_dist$q(0.99) # generate quantile values as a sequence for each natural number si_discrete_x <- seq(1L, to = x_limit, by = 1L) # calculate the values for each quantiles in the density function -si_discrete_y <- covid_lnorm_discrete$prob_dist$d(si_discrete_x) +si_discrete_y <- covid_serialint_discrete$prob_dist$d(si_discrete_x) tibble( quantile_values = si_discrete_x, @@ -527,9 +618,9 @@ Now we are ready to plug-in all to the `EpiNow2::dist_spec()` function! ```{r} generation_time_epiparam <- dist_spec( - mean = covid_lnorm$summary_stats$mean, - sd = covid_lnorm$summary_stats$sd, - max = covid_lnorm_discrete_max, + mean = covid_serialint$summary_stats$mean, + sd = covid_serialint$summary_stats$sd, + max = covid_serialint_discrete_max, distribution = "lognormal" ) @@ -579,7 +670,7 @@ The natural history of a disease influence the transmission dynamics. For `{EpiN ![The relationship between the incubation period and serial interval. From [Nishiura 2020](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext)](fig/incubation-period-serial-interval.jpg) -![Parameter estimates. Plausible ranges for the key parameters R0 and θ (see main text for sources) for four viral infections of public concern are shown as shaded regions. The size of the shaded area reflects the uncertainties in the parameter estimates. [Fraser et al., 2004](https://www.pnas.org/doi/10.1073/pnas.0307506101)](fig/reproduction-number-pre-symptomatic.png) +![Parameter estimates. Plausible ranges for the key parameters R0 and θ (read main text for sources) for four viral infections of public concern are shown as shaded regions. The size of the shaded area reflects the uncertainties in the parameter estimates. [Fraser et al., 2004](https://www.pnas.org/doi/10.1073/pnas.0307506101)](fig/reproduction-number-pre-symptomatic.png) ::::::::::::::::::::::::::::::::: challenge @@ -675,7 +766,7 @@ covid_incubation covid_incubation$summary_stats$mean covid_incubation_discrete <- epiparameter::discretise(covid_incubation) -covid_incubation_discrete_max <- covid_lnorm_discrete$prob_dist$q(p = 0.999) +covid_incubation_discrete_max <- covid_serialint_discrete$prob_dist$q(p = 0.999) incubation_time_epiparam <- dist_spec( mean = covid_incubation$summary_stats$mean, @@ -793,7 +884,7 @@ The three examples above represent transmission parameters. An interesting example is the `method_assess` nested entry, which refers to the methods used to assess for the bias while estimating the serial interval distribution. ```{r} -covid_lnorm$method_assess +covid_serialint$method_assess ``` From a1065e580fa785f0f8f0910bb033b16198d63337 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 14 Dec 2023 03:19:25 +0000 Subject: [PATCH 070/130] add normal distribution figure --- episodes/fig/fig5a-normaldistribution.png | Bin 0 -> 111729 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 episodes/fig/fig5a-normaldistribution.png diff --git a/episodes/fig/fig5a-normaldistribution.png b/episodes/fig/fig5a-normaldistribution.png new file mode 100644 index 0000000000000000000000000000000000000000..c6bcbb8608b3f701d52d8a04b07f441624542cc6 GIT binary patch literal 111729 zcmd4YV{j$m+c5Yf6UMFn{h@z-h5nWmgZ+v569fbVRzh4@0R#lh7X$><>O17undHy)y{`kfgP?@c z_piLproTo=fqG27uqzlG@fp)Bg{Vn4?AC;a9lI4;6t?#7fKq#(q;1w7C=q^?`=_p-_ 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For this we need to access to the set of distribution functions for our `` object. +One way to do this is getting the quantile value for the the 99.9th percentile or `0.999` cumulative probability of the distribution. For this we need to access to the set of distribution functions for our `` object. -We can use the set of [distribution functions](https://epiverse-trace.github.io/epiparameter/articles/epiparameter.html#distribution-functions) for a continuous distribution. However, this values will be _continuous_ numbers. To get discrete values from a continuous distribution we can **discretize** the continuous distribution stored in our `` object. +We can use the set of distribution functions for a _continuous_ distribution (as above). However, this values will be _continuous_ numbers. To get discrete values from a continuous distribution we can **discretize** the continuous distribution stored in our `` object. -The epidemiological parameter stored in `covid_serialint` is a **continuous** distribution. Here we plot its *Probability Density Function (PDF)*: - -```{r} -plot(covid_serialint) -``` - -When we `epiparameter::discretise()` the continuous distribution we get a **discrete** distribution: +When we `epiparameter::discretise()` the continuous distribution we get a **discrete**(-ized) distribution: ```{r} covid_serialint_discrete <- epiparameter::discretise(covid_serialint) @@ -547,48 +541,71 @@ We identify this change in the `Distribution:` output line of the `` ob Distribution: discrete lnorm ``` +For a **continuous** distribution, we plot the *Probability Density Function (PDF)*: + +```{r,eval=FALSE} +plot(covid_serialint) +``` + For a **discrete** distribution, we plot the *Probability Mass Function (PMF)*: ```{r} plot(covid_serialint_discrete) ``` -To finally get a `max` value let's access to the quantile value of the 99th percentile or `0.99` probability of the distribution with the `prob_dist$q` notation, similarly to how we access to the `summary_stats` values. +To finally get a `max` value let's access to the quantile value of the 99.9th percentile or `0.999` probability of the distribution with the `prob_dist$q` notation, similarly to how we access to the `summary_stats` values. ```{r} -covid_serialint_discrete_max <- covid_serialint_discrete$prob_dist$q(p = 0.99) +covid_serialint_discrete_max <- covid_serialint_discrete$prob_dist$q(p = 0.999) ``` ::::::::::::::::::::::::::::::::: challenge -### 99th percentile of the incubation period +### 99th percentile of the incubation period of COVID-19 -Within what time frame do 99% of individuals who develop symptoms exhibit them after infection? +Within what time frame do 99% of individuals who develop COVID-19 symptoms exhibit them after infection? ::::::::::::::::: hint -```{r} -plot(covid_serialint_discrete,day_range = 0:20) +First, we are asking about a different distribution delay: incubation period + +Probability function for `` **discrete** distributions differ from the *continuous* ones! + +```{r,eval=FALSE} +# plot to have a visual reference +plot(covid_serialint_discrete, day_range = 0:20) + +# density value at quantile value 10 (day) +covid_serialint_discrete$prob_dist$d(10) + +# cumulative probability at quantile value 10 (day) +covid_serialint_discrete$prob_dist$cdf(10) + +# in what quantile value (days) we have the 60% cumulative probability? +covid_serialint_discrete$prob_dist$q(0.6) +# covid_serialint_discrete$prob_dist$qf(0.6) # as in continuous distribution + # generate random values -covid_serialint_discrete$prob_dist$r(1000) -# in what quantile we have the 99.9% cumulative probability? -covid_serialint_discrete$prob_dist$q(0.99) -# covid_serialint_discrete$prob_dist$qf(0.99) # as in continuous distribution? -x_limit <- covid_serialint_discrete$prob_dist$q(0.99) -# generate quantile values as a sequence for each natural number -si_discrete_x <- seq(1L, to = x_limit, by = 1L) -# calculate the values for each quantiles in the density function -si_discrete_y <- covid_serialint_discrete$prob_dist$d(si_discrete_x) +covid_serialint_discrete$prob_dist$r(10) + +# create a discrete distribution visualization +x_limit <- covid_serialint_discrete$prob_dist$q(0.999) tibble( - quantile_values = si_discrete_x, - density_values = si_discrete_y -) %>% - ggplot(aes(quantile_values,density_values)) + + # generate quantile values as a sequence for each natural number + quantile_values = seq(1L, to = x_limit, by = 1L) +) %>% + mutate( + # calculate the values for each quantiles in the density function + density_values = covid_serialint_discrete$prob_dist$d(quantile_values) + ) %>% + ggplot(aes( + x = quantile_values, + y = density_values) + ) + geom_col() ``` - :::::::::::::::::::::: ::::::::::::::::: solution @@ -606,13 +623,17 @@ covid_incubation_discrete <- epiparameter::discretise(covid_incubation) covid_incubation_discrete$prob_dist$q(0.99) ``` -99% of those who develop symptoms will do so within 16 days of infection. +99% of those who develop COVID-19 symptoms will do so within 16 days of infection. + +_Dive into the structure of this new `` R object for discrete distributions!_ :::::::::::::::::::::::::: ::::::::::::::::::::::::::::::::::::::::::: +## Plug-in `{epiparameter}` to `{EpiNow2}` + Now we are ready to plug-in all to the `EpiNow2::dist_spec()` function! ```{r} From 3255ae020751600ef7505a327183fe2c9ddf3dd9 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 14 Dec 2023 04:03:11 +0000 Subject: [PATCH 072/130] relevel titles to isolate content index --- episodes/read-delays.Rmd | 39 ++++++++++++++++++++++++++------------- 1 file changed, 26 insertions(+), 13 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 440642c7..7fa598a0 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -73,7 +73,7 @@ library(tidyverse) ``` -## Find a generation time +## Find a Generation time The generation time, jointly with the $R$, can inform about the speed of spread and its feasibility of control. Given a $R>1$, with a shorter generation time, cases can appear more quickly. @@ -648,6 +648,14 @@ generation_time_epiparam <- generation_time_epiparam ``` +:::::::::: callout + +### Warning + +Using the serial interval instead of the generation time is an alternative that can propagate bias in your estimates. Even more in diseases with reported pre-symptomatic transmission! + +:::::::::::::::::: + Now, let's replace the `generation_time` input that we used for `EpiNow2::epinow()`. ```{r} @@ -682,7 +690,7 @@ epidist_db(disease = "influenza") %>% # if not, find serial interval and use it as serial interval ``` -## Find the incubation period +## Add the Incubation period What it is? With which other is related? @@ -819,7 +827,7 @@ base::plot(epinow_estimates) ``` -## formative +### formative ```{r} # formative, now with ebola ----------------------------------------------- @@ -834,7 +842,7 @@ Potential biases like - censoring bias (short periods) - reporting bias (recent contacts) -## Find the reporting delay +## Add the Reporting delays What it is? With which other is related? @@ -847,8 +855,12 @@ The natural history of a disease influence the transmission dynamics. For `{EpiN ![Timeline for chain of disease reporting, the Netherlands. Lab, laboratory; PHA, public health authority. From [Marinović et al., 2015](https://wwwnc.cdc.gov/eid/article/21/2/13-0504_article)](fig/disease-reporting.jpg) +### Convert from statistical summaries to distribution parameters + +https://github.com/epiverse-trace/epiparameter/issues/114 -## any epiparameter? + +### any epiparameter? ```{r} epidist_db(disease = "all") %>% list_distributions() %>% @@ -856,7 +868,7 @@ epidist_db(disease = "all") %>% list_distributions() %>% distinct(epi_distribution) ``` -## replace in epinow2 +### replace in epinow2 ```{r} reporting_delay <- dist_spec( @@ -883,7 +895,7 @@ epinow_estimates <- epinow( ) ``` -## formative +### formative ```{r} ### 3 add reporting delay -------- @@ -900,6 +912,12 @@ The three examples above represent transmission parameters. - Duration of hospitalization (bed capacity needed) +### explore + +```{r} + +``` + ## From our data! (callout) An interesting example is the `method_assess` nested entry, which refers to the methods used to assess for the bias while estimating the serial interval distribution. @@ -908,7 +926,6 @@ An interesting example is the `method_assess` nested entry, which refers to the covid_serialint$method_assess ``` - Fit a parametric distribution to estimate the mean and sd estimate serial interval from case and contact data pairs @@ -919,11 +936,7 @@ ref: https://epiverse-trace.github.io/epimodelac/EnfermedadX.html Recall that "Methods for accurate estimation of the generation interval from contact tracing data involve adjusting for right truncation and accounting for population susceptibility at the times transmission pairs are observed" [(Gostic et al., 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409) -## Convert from statistical summaries to distribution parameters - -https://github.com/epiverse-trace/epiparameter/issues/114 - -## concept map +## Concept map update it from last epiparameter test From 46e87d3845b244de8190ed7e059010669ef0346f Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 14 Dec 2023 05:08:52 +0000 Subject: [PATCH 073/130] add formative assessment + block all epinow2 --- episodes/read-delays.Rmd | 164 +++++++++++++++++++++++++++++++++------ 1 file changed, 139 insertions(+), 25 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 7fa598a0..687e2271 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -637,7 +637,7 @@ _Dive into the structure of this new `` R object for discrete distribut Now we are ready to plug-in all to the `EpiNow2::dist_spec()` function! ```{r} -generation_time_epiparam <- +serial_interval_covid <- dist_spec( mean = covid_serialint$summary_stats$mean, sd = covid_serialint$summary_stats$sd, @@ -645,7 +645,7 @@ generation_time_epiparam <- distribution = "lognormal" ) -generation_time_epiparam +serial_interval_covid ``` :::::::::: callout @@ -658,12 +658,12 @@ Using the serial interval instead of the generation time is an alternative that Now, let's replace the `generation_time` input that we used for `EpiNow2::epinow()`. -```{r} +```{r,eval=FALSE} epinow_estimates <- epinow( # cases reported_cases = example_confirmed[1:60], # delays - generation_time = generation_time_opts(generation_time_epiparam), + generation_time = generation_time_opts(serial_interval_covid), # computation stan = stan_opts( cores = 4, samples = 1000, chains = 3, @@ -674,22 +674,76 @@ epinow_estimates <- epinow( base::plot(epinow_estimates) ``` -### formative +::::::::::::::::::::::::::::::::: challenge + +### EBOLA + +Download the [Ebola dataset](data/ebola_cases.csv): + +- Reuse one epidemiological parameter to estimate the effective reproductive number for the Ebola dataset. ```{r} -# what parameters are are available for influenza? -epidist_db(disease = "influenza") %>% - list_distributions() %>% - as_tibble() %>% - count(epi_distribution) +ebola_confirmed <- read_csv("episodes/data/ebola_cases.csv") ``` +::::::::::::::::: hint + +Key functions we applied until now: + +- `epidist_db` +- `list_distributions` +- `discretise` +- probability functions + +Review the type of distribution of the parameter you choose. + +:::::::::::::::::::::: + +::::::::::::::::: solution + +```{r,message=FALSE,warning=FALSE,eval=FALSE} +# read data +ebola_confirmed <- read_csv("episodes/data/ebola_cases.csv") + +# list distributions +epidist_db(disease = "ebola") %>% + list_distributions() + +# subset one distribution +ebola_serial <- epidist_db(disease = "ebola", epi_dist = "serial", single_epidist = T) + +ebola_serial_discrete <- discretise(ebola_serial) + +serial_interval_ebola <- + dist_spec( + mean = ebola_serial$summary_stats$mean, + sd = ebola_serial$summary_stats$sd, + max = ebola_serial_discrete$prob_dist$q(p = 0.999), + distribution = "gamma" + ) + +epinow_estimates <- epinow( + # cases + reported_cases = ebola_confirmed, + # distribution + generation_time = generation_time_opts(serial_interval_ebola), + # computation + stan = stan_opts( + cores = 4, samples = 1000, chains = 3, + control = list(adapt_delta = 0.99) + ) +) + +plot(epinow_estimates) -```{r} -# generation time for ebola -# if not, find serial interval and use it as serial interval ``` +:::::::::::::::::::::::::: + + +::::::::::::::::::::::::::::::::::::::::::: + + ## Add the Incubation period What it is? @@ -701,7 +755,6 @@ The natural history of a disease influence the transmission dynamics. For `{EpiN ![Parameter estimates. Plausible ranges for the key parameters R0 and θ (read main text for sources) for four viral infections of public concern are shown as shaded regions. The size of the shaded area reflects the uncertainties in the parameter estimates. [Fraser et al., 2004](https://www.pnas.org/doi/10.1073/pnas.0307506101)](fig/reproduction-number-pre-symptomatic.png) - ::::::::::::::::::::::::::::::::: challenge ### WHAT TYPE OF TRANSMISSION? @@ -753,6 +806,8 @@ epidist_db(disease = "ebola",epi_dist = "serial",single_epidist = T)$summary_sta +### example + ```{r} incubation_period <- dist_spec( mean = 4, sd = 2, @@ -760,7 +815,7 @@ incubation_period <- dist_spec( ) ``` -```{r,message=FALSE,warning=FALSE,cache=TRUE} +```{r,message=FALSE,warning=FALSE,cache=TRUE,eval=FALSE} epinow_estimates <- epinow( # cases reported_cases = example_confirmed[1:60], @@ -776,7 +831,7 @@ epinow_estimates <- epinow( ``` -```{r} +```{r,eval=FALSE} ### 2 include incubation time ------ epiparameter::epidist_db( @@ -808,7 +863,7 @@ epinow_estimates <- epinow( # cases reported_cases = example_confirmed[1:60], # generation time - generation_time = generation_time_opts(generation_time_epiparam), + generation_time = generation_time_opts(serial_interval_covid), # delays delays = delay_opts(incubation_time_epiparam), # computation @@ -827,8 +882,68 @@ base::plot(epinow_estimates) ``` +## Extract parameters + +https://github.com/epiverse-trace/epiparameter/issues/114 + + ### formative +```{r,eval=FALSE} +# what parameters are are available for influenza? +epidist_db(disease = "influenza") %>% + list_distributions() %>% + as_tibble() %>% + count(epi_distribution) + +influenza_generation <- epidist_db(disease = "influenza",epi_dist = "generation") + +influenza_generation_discrete <- discretise(influenza_generation) + +influenza_generation$summary_stats$median +influenza_generation$summary_stats$quantiles + +# question: is dispersion valid for quantiles? +# convert_summary_stats_to_params( +# distribution = "lnorm", +# median = influenza_generation$summary_stats$median, +# quantiles = influenza_generation$summary_stats$quantiles) + +influenza_extracted <- extract_param( + type = "percentiles", + values = c(influenza_generation$summary_stats$quantiles[1], + influenza_generation$summary_stats$quantiles[2]), + distribution = "lnorm", + percentiles = c(0.05,0.95) +) + +generation_time_influenza <- + dist_spec( + mean = influenza_extracted[1], + sd = influenza_extracted[2], + max = influenza_generation_discrete$prob_dist$q(p = 0.999), + distribution = "lognormal" + ) + +influenza_cleaned <- outbreaks::influenza_england_1978_school %>% + select(date,confirm = in_bed) + +epinow_estimates <- epinow( + # cases + reported_cases = influenza_cleaned, + # distribution + generation_time = generation_time_opts(generation_time_influenza), + # computation + stan = stan_opts( + cores = 4, samples = 1000, chains = 3, + control = list(adapt_delta = 0.99) + ) +) + +plot(epinow_estimates) +``` + + ```{r} # formative, now with ebola ----------------------------------------------- @@ -837,7 +952,7 @@ base::plot(epinow_estimates) ``` -Potential biases like +However, this still propagates potential biases like - censoring bias (short periods) - reporting bias (recent contacts) @@ -855,11 +970,6 @@ The natural history of a disease influence the transmission dynamics. For `{EpiN ![Timeline for chain of disease reporting, the Netherlands. Lab, laboratory; PHA, public health authority. From [Marinović et al., 2015](https://wwwnc.cdc.gov/eid/article/21/2/13-0504_article)](fig/disease-reporting.jpg) -### Convert from statistical summaries to distribution parameters - -https://github.com/epiverse-trace/epiparameter/issues/114 - - ### any epiparameter? ```{r} @@ -878,7 +988,9 @@ reporting_delay <- dist_spec( ) ``` -```{r,message=FALSE,warning=FALSE,cache=TRUE} +### epinow2 + +```{r,message=FALSE,warning=FALSE,cache=TRUE,eval=FALSE} epinow_estimates <- epinow( # cases reported_cases = example_confirmed[1:60], @@ -915,7 +1027,7 @@ The three examples above represent transmission parameters. ### explore ```{r} - +# from data set ``` ## From our data! (callout) @@ -936,9 +1048,11 @@ ref: https://epiverse-trace.github.io/epimodelac/EnfermedadX.html Recall that "Methods for accurate estimation of the generation interval from contact tracing data involve adjusting for right truncation and accounting for population susceptibility at the times transmission pairs are observed" [(Gostic et al., 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409) + ::::::::::::::::::::::::::::::::::::: keypoints From 5a6a990bf48765359025a2fcdae8744668619844 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 14 Dec 2023 05:34:57 +0000 Subject: [PATCH 074/130] write the structure of content --- episodes/read-delays.Rmd | 121 +++++++++++++++++++++------------------ 1 file changed, 64 insertions(+), 57 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 687e2271..a6631ac2 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -746,24 +746,32 @@ plot(epinow_estimates) ## Add the Incubation period -What it is? -With which other is related? +For `{EpiNow2}`, we can specify relevant delay distributions like the [incubation period](../learners/reference.md#incubation). -The natural history of a disease influence the transmission dynamics. For `{EpiNow2}`, we can specify relevant delay distributions like the [incubation period](../learners/reference.md#incubation): +The lengths of the Serial interval and Incubation period determine the type of transmission of a disease. ![The relationship between the incubation period and serial interval. From [Nishiura 2020](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext)](fig/incubation-period-serial-interval.jpg) +The proportion of pre-symptomatic transmission contributes to the effective reproductive number and expected new cases to have. + ![Parameter estimates. Plausible ranges for the key parameters R0 and θ (read main text for sources) for four viral infections of public concern are shown as shaded regions. The size of the shaded area reflects the uncertainties in the parameter estimates. [Fraser et al., 2004](https://www.pnas.org/doi/10.1073/pnas.0307506101)](fig/reproduction-number-pre-symptomatic.png) ::::::::::::::::::::::::::::::::: challenge ### WHAT TYPE OF TRANSMISSION? -... +Compare the serial interval and incubation period of Influenza and MERS: + +- What type of transmission have Influenza? +- What type of transmission have MERS? +- Does these results correlates with the available evidence? ::::::::::::::::: hint -... +Key functions: + +- `epidist_db()` +- `epidist$summary_stats$` :::::::::::::::::::::: @@ -779,7 +787,7 @@ epidist_db(disease = "mers",epi_dist = "incubation",single_epidist = T)$summary_ epidist_db(disease = "mers",epi_dist = "serial",single_epidist = T)$summary_stats$mean ``` -```{r} +```{r,eval=FALSE} # pre-symptomatic transmission epidist_db(disease = "covid",epi_dist = "incubation",author = "Stephen",single_epidist = T)$summary_stats$mean epidist_db(disease = "covid",epi_dist = "serial",author = "Nishiura",single_epidist = T)$summary_stats$mean @@ -806,22 +814,25 @@ epidist_db(disease = "ebola",epi_dist = "serial",single_epidist = T)$summary_sta -### example +::::::::::::::::::::::::::::::::: challenge -```{r} -incubation_period <- dist_spec( - mean = 4, sd = 2, - max = 20, distribution = "gamma" -) -``` +### Reuse an Incubation period for COVID-19 -```{r,message=FALSE,warning=FALSE,cache=TRUE,eval=FALSE} +Use `{epiparameter}` to: + +- Find and incubation period for COVID-19 +- Add to our last `epinow()` code chunk the `incubation_time_covid` in the `delays` argument. + +::::::::::::::::: hint + +```r epinow_estimates <- epinow( # cases reported_cases = example_confirmed[1:60], + # generation time + generation_time = generation_time_opts(serial_interval_covid), # delays - generation_time = generation_time_opts(generation_time), - delays = delay_opts(incubation_period), + delays = delay_opts(incubation_time_covid), # computation stan = stan_opts( cores = 4, samples = 1000, chains = 3, @@ -830,15 +841,11 @@ epinow_estimates <- epinow( ) ``` +:::::::::::::::::::::: -```{r,eval=FALSE} -### 2 include incubation time ------ - -epiparameter::epidist_db( - disease = "covid", - epi_dist = "incubation") %>% - epiparameter::list_distributions() +::::::::::::::::: solution +```{r,eval=FALSE} covid_incubation <- epiparameter::epidist_db( disease = "covid", epi_dist = "incubation", @@ -848,14 +855,12 @@ covid_incubation <- epiparameter::epidist_db( covid_incubation -covid_incubation$summary_stats$mean covid_incubation_discrete <- epiparameter::discretise(covid_incubation) -covid_incubation_discrete_max <- covid_serialint_discrete$prob_dist$q(p = 0.999) -incubation_time_epiparam <- dist_spec( +incubation_time_covid <- dist_spec( mean = covid_incubation$summary_stats$mean, sd = covid_incubation$summary_stats$sd, - max = covid_incubation_discrete_max, + max = covid_serialint_discrete$prob_dist$q(p = 0.999), distribution = "lognormal" ) @@ -865,7 +870,7 @@ epinow_estimates <- epinow( # generation time generation_time = generation_time_opts(serial_interval_covid), # delays - delays = delay_opts(incubation_time_epiparam), + delays = delay_opts(incubation_time_covid), # computation stan = stan_opts( cores = 4, samples = 1000, chains = 3, @@ -874,17 +879,28 @@ epinow_estimates <- epinow( ) base::plot(epinow_estimates) +``` -# post calculation questions -# what have changed? -# - cases by date of infection -# - uncertainty +:::::::::::::::::::::::::: -``` + +::::::::::::::::::::::::::::::::::::::::::: + + +:::::::::::::: discussion + +### How much it changed? + +After adding the incubation period, discuss: + +- Does the retrospective trend of forecast changed? +- Does the uncertainty changed? + +:::::::::::::::::::::::::::: ## Extract parameters -https://github.com/epiverse-trace/epiparameter/issues/114 + ### formative @@ -943,6 +959,7 @@ epinow_estimates <- epinow( plot(epinow_estimates) ``` +### formative ```{r} # formative, now with ebola ----------------------------------------------- @@ -959,14 +976,11 @@ However, this still propagates potential biases like ## Add the Reporting delays -What it is? -With which other is related? - -Adjusting for delays +Estimating Rt requires data on the daily number of new infections (i.e., transmission events). Due to lags in the development of detectable viral loads, symptom onset, seeking care, and reporting, these numbers are not readily available. All observations reflect transmission events from some time in the past. In other words, if d is the delay from infection to observation, then observations at time t inform Rt−d, not Rt. [(Gostic et al., 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409#sec007) ![Rt is a measure of transmission at time t. Observations after time t must be adjusted. ICU, intensive care unit. From [Gostic et al., 2020](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409#sec007)](fig/rt-adjusting-delays.png) -The natural history of a disease influence the transmission dynamics. For `{EpiNow2}`, we can specify relevant delay distributions like the [reporting delay](../learners/reference.md#reportingdelay): +We can opt to adjust `{EpiNow2}` outputs by the [reporting delay](../learners/reference.md#reportingdelay) that condition the report of confirmed cases. ![Timeline for chain of disease reporting, the Netherlands. Lab, laboratory; PHA, public health authority. From [Marinović et al., 2015](https://wwwnc.cdc.gov/eid/article/21/2/13-0504_article)](fig/disease-reporting.jpg) @@ -981,16 +995,18 @@ epidist_db(disease = "all") %>% list_distributions() %>% ### replace in epinow2 ```{r} + +``` + +### epinow2 + +```{r,message=FALSE,warning=FALSE,cache=TRUE,eval=FALSE} reporting_delay <- dist_spec( mean = convert_to_logmean(2, 1), sd = convert_to_logsd(2, 1), max = 10, distribution = "lognormal" ) -``` - -### epinow2 -```{r,message=FALSE,warning=FALSE,cache=TRUE,eval=FALSE} epinow_estimates <- epinow( # cases reported_cases = example_confirmed[1:60], @@ -1007,27 +1023,18 @@ epinow_estimates <- epinow( ) ``` -### formative - -```{r} -### 3 add reporting delay -------- - -# now is your turn! -# after the ebola assessment - - -``` - ## Severity parameters The three examples above represent transmission parameters. -- Duration of hospitalization (bed capacity needed) +One example of severity parameter is the duration of hospitalization (bed capacity needed) ### explore ```{r} -# from data set +epidist_db(disease = "all") %>% list_distributions() %>% + as_tibble() %>% + distinct(epi_distribution) ``` ## From our data! (callout) @@ -1042,7 +1049,7 @@ Fit a parametric distribution to estimate the mean and sd estimate serial interval from case and contact data pairs -ref: https://www.reconlearn.org/post/real-time-response-2 + ref: https://epiverse-trace.github.io/epimodelac/EnfermedadX.html From e8f858487fbe639881a52fafee34dd3f51c01e0a Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 14 Dec 2023 19:05:27 +0000 Subject: [PATCH 075/130] move questions to section on from your data --- episodes/read-delays.Rmd | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index a6631ac2..af4dd3c0 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -10,12 +10,7 @@ editor_options: - how to obtain delay distributions from a literature systematic review? - what is the natural history of the disease? -- how long should contacts be followed? -- what is the required duration of contact tracing? -- how long should cases be isolated to reduce transmission? - - what are the epidemiological characteristics of the infection? -- what is the demographic distribution of cases? :::::::::::::::::::::::::::::::::::::::::::::::: @@ -1039,6 +1034,10 @@ epidist_db(disease = "all") %>% list_distributions() %>% ## From our data! (callout) +- how long should contacts be followed? +- what is the required duration of contact tracing? +- how long should cases be isolated to reduce transmission? + An interesting example is the `method_assess` nested entry, which refers to the methods used to assess for the bias while estimating the serial interval distribution. ```{r} From 6ef20b13e4c1a4c8989ca8162e37b0a7c8604dfe Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 14 Dec 2023 19:05:53 +0000 Subject: [PATCH 076/130] translate questions to cases --- episodes/describe-cases.Rmd | 1 + 1 file changed, 1 insertion(+) diff --git a/episodes/describe-cases.Rmd b/episodes/describe-cases.Rmd index f56c99d6..63236b57 100644 --- a/episodes/describe-cases.Rmd +++ b/episodes/describe-cases.Rmd @@ -8,6 +8,7 @@ exercises: 2 - How do you visualize incidence cases? - How to get serial interval from case data? +- what is the demographic distribution of cases? :::::::::::::::::::::::::::::::::::::::::::::::: From 4c4f9bb77472d4f57cb41a8025084be559ff7016 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 14 Dec 2023 19:06:59 +0000 Subject: [PATCH 077/130] update title --- episodes/read-delays.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index af4dd3c0..2090e545 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -1,5 +1,5 @@ --- -title: 'Describe case data' +title: 'Read delays' teaching: 10 exercises: 2 editor_options: From 9e079adcfe87cca5ed4b1037691659d81fe83f4f Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 16 Dec 2023 21:52:57 +0000 Subject: [PATCH 078/130] update questions and objectives add sections and package names --- episodes/describe-cases.Rmd | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/episodes/describe-cases.Rmd b/episodes/describe-cases.Rmd index 63236b57..8e9d1f7b 100644 --- a/episodes/describe-cases.Rmd +++ b/episodes/describe-cases.Rmd @@ -7,8 +7,8 @@ exercises: 2 :::::::::::::::::::::::::::::::::::::: questions - How do you visualize incidence cases? -- How to get serial interval from case data? -- what is the demographic distribution of cases? +- what is the growth rate of the epidemic? +- what is the person, place, time distribution of cases? :::::::::::::::::::::::::::::::::::::::::::::::: @@ -16,6 +16,7 @@ exercises: 2 - Create incidence curves - Estimate the growth rate from incidence curves +- Create quick descriptive and comparison tables :::::::::::::::::::::::::::::::::::::::::::::::: @@ -23,13 +24,23 @@ exercises: 2 This is a lesson created via The Carpentries Workbench. +## time + ## Exponential growth Within the renewal equation, the generation interval mechanistically links the reproductive number R to observables such as the epidemic growth rate r or the number of new infections per day [(Wallinga et al. 2006)](https://royalsocietypublishing.org/doi/10.1098/rspb.2006.3754) +## person + +## place + + + ::::::::::::::::::::::::::::::::::::: keypoints - Use `{incidence2}` +- Use `{i2extras}` +- Use `{compareGroups}` :::::::::::::::::::::::::::::::::::::::::::::::: From 044f0dce1bea21cea90f9f8866b2a1bd61b9506d Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 16 Dec 2023 21:53:19 +0000 Subject: [PATCH 079/130] add question on serial interval from data --- episodes/read-delays.Rmd | 1 + 1 file changed, 1 insertion(+) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 2090e545..2b915b6e 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -1034,6 +1034,7 @@ epidist_db(disease = "all") %>% list_distributions() %>% ## From our data! (callout) +- How to get serial interval from case data? - how long should contacts be followed? - what is the required duration of contact tracing? - how long should cases be isolated to reduce transmission? From c3712e4c4f74cbd433bf4a2e2b8eec8066b3e207 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 16 Dec 2023 22:22:14 +0000 Subject: [PATCH 080/130] move read-cases file to separate branch new branch https://github.com/epiverse-trace/tutorials/tree/early-task-read --- episodes/read-cases.Rmd | 37 ------------------------------------- 1 file changed, 37 deletions(-) delete mode 100644 episodes/read-cases.Rmd diff --git a/episodes/read-cases.Rmd b/episodes/read-cases.Rmd deleted file mode 100644 index 23449314..00000000 --- a/episodes/read-cases.Rmd +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: 'Read case data' -teaching: 10 -exercises: 2 ---- - -:::::::::::::::::::::::::::::::::::::: questions - -- how many different data formats can I read? -- why should I have to validated a linelist? -- how to check for age and date variables? - -:::::::::::::::::::::::::::::::::::::::::::::::: - -::::::::::::::::::::::::::::::::::::: objectives - -- Explain how to use markdown with the new lesson template -- Demonstrate how to include pieces of code, figures, and nested challenge blocks - -:::::::::::::::::::::::::::::::::::::::::::::::: - -## Introduction - -This is a lesson created via The Carpentries Workbench. - -# Section - -Bla - -::::::::::::::::::::::::::::::::::::: keypoints - -- Use `{readepi}` to read data using a consistent and unified interface. -- Use `{cleanepi}` to apply multiple cleaning steps in one function. -- Use `{linelist}` to validate and safeguard your data early in the pipeline. - -:::::::::::::::::::::::::::::::::::::::::::::::: - From b9ca407f2374c9296e5c273c08718e508b7feb4b Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 16 Dec 2023 22:23:08 +0000 Subject: [PATCH 081/130] move describe-cases to separate branch new branch https://github.com/epiverse-trace/tutorials/tree/early-task-describe --- episodes/describe-cases.Rmd | 46 ------------------------------------- 1 file changed, 46 deletions(-) delete mode 100644 episodes/describe-cases.Rmd diff --git a/episodes/describe-cases.Rmd b/episodes/describe-cases.Rmd deleted file mode 100644 index 8e9d1f7b..00000000 --- a/episodes/describe-cases.Rmd +++ /dev/null @@ -1,46 +0,0 @@ ---- -title: 'describe-cases' -teaching: 10 -exercises: 2 ---- - -:::::::::::::::::::::::::::::::::::::: questions - -- How do you visualize incidence cases? -- what is the growth rate of the epidemic? -- what is the person, place, time distribution of cases? - -:::::::::::::::::::::::::::::::::::::::::::::::: - -::::::::::::::::::::::::::::::::::::: objectives - -- Create incidence curves -- Estimate the growth rate from incidence curves -- Create quick descriptive and comparison tables - -:::::::::::::::::::::::::::::::::::::::::::::::: - -## Introduction - -This is a lesson created via The Carpentries Workbench. - -## time - -## Exponential growth - -Within the renewal equation, the generation interval mechanistically links the reproductive number R to observables such as the epidemic growth rate r or the number of new infections per day [(Wallinga et al. 2006)](https://royalsocietypublishing.org/doi/10.1098/rspb.2006.3754) - -## person - -## place - - - -::::::::::::::::::::::::::::::::::::: keypoints - -- Use `{incidence2}` -- Use `{i2extras}` -- Use `{compareGroups}` - -:::::::::::::::::::::::::::::::::::::::::::::::: - From 1c1796f693343fb7b83eb31c0d4ba5a6d3f79b00 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 16 Dec 2023 22:23:45 +0000 Subject: [PATCH 082/130] move create-report to separate branch new branch https://github.com/epiverse-trace/tutorials/tree/early-task-report --- episodes/create-report.Rmd | 28 ---------------------------- 1 file changed, 28 deletions(-) delete mode 100644 episodes/create-report.Rmd diff --git a/episodes/create-report.Rmd b/episodes/create-report.Rmd deleted file mode 100644 index 27e34662..00000000 --- a/episodes/create-report.Rmd +++ /dev/null @@ -1,28 +0,0 @@ ---- -title: 'create-report' -teaching: 10 -exercises: 2 ---- - -:::::::::::::::::::::::::::::::::::::: questions - -- How to write a reproducible report with `{episoap}`? - -:::::::::::::::::::::::::::::::::::::::::::::::: - -::::::::::::::::::::::::::::::::::::: objectives - -- Create a reproducible situation report. - -:::::::::::::::::::::::::::::::::::::::::::::::: - -## Introduction - -This is a lesson created via The Carpentries Workbench. - -::::::::::::::::::::::::::::::::::::: keypoints - -- Use `{episoap}` to - -:::::::::::::::::::::::::::::::::::::::::::::::: - From 45c98138770500dd08b82b67f710c5272b26889c Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sun, 17 Dec 2023 15:09:44 +0000 Subject: [PATCH 083/130] add chunk placeholder for reactive hexwall --- episodes/introduction.Rmd | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index e0ff5cf8..23dd35a7 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -167,6 +167,30 @@ Epiverse-TRACE aim is to provide a software ecosystem for outbreak analytics. We ![](fig/pkgs-hexlogos.png) +```{r, results='asis', echo = FALSE, eval=FALSE} +# eval = TRUE +# turn on if maintainers manage to +# adapt css from main page to varnish +pkg_list <- c("readepi","cleanepi","linelist","epiparameter","episoap") + +# from https://github.com/epiverse-trace/epiverse-trace.github.io/blob/main/index.qmd + +pkg_path <- "https://raw.githubusercontent.com/epiverse-trace/epiverse-trace.github.io/main/_data/epiverse_pkgs.csv" +hexwall_path<- "https://raw.githubusercontent.com/epiverse-trace/epiverse-trace.github.io/main/_templates/hexwall_element.Rmd" + +file.path(pkg_path) |> + read.csv() |> + # dplyr::filter( + # magrittr::is_in(pkgname,pkg_list)) |> + as.list() |> + purrr::transpose() |> + purrr::map_chr(~ knitr::knit_expand( + file = hexwall_path, + pkgname = .x$pkgname, + description = .x$description)) |> + cat(sep = "\n") +``` + ## How can I start? Our plan for these tutorials is to introduce key solutions from packages in all the tasks before and after the _Quantify transmission_ task, plus the required theory concepts to interpret modelling outputs and make rigorous conclusions. From 9a67e7149fa9acbba3e79a90fe77f5e7e735aa9f Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sun, 17 Dec 2023 15:36:31 +0000 Subject: [PATCH 084/130] rephrase questions, move one to other branch --- episodes/read-delays.Rmd | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 2b915b6e..d65fbad9 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -8,9 +8,8 @@ editor_options: :::::::::::::::::::::::::::::::::::::: questions -- how to obtain delay distributions from a literature systematic review? +- how to get delay distributions from a systematic review? - what is the natural history of the disease? -- what are the epidemiological characteristics of the infection? :::::::::::::::::::::::::::::::::::::::::::::::: From 09498d4bd0f1615ae97e161780e4fc6a43a55924 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sun, 17 Dec 2023 18:33:15 +0000 Subject: [PATCH 085/130] add rcompendium paths to data as suggested in #90 --- episodes/read-delays.Rmd | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index d65fbad9..ebf493da 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -676,8 +676,8 @@ Download the [Ebola dataset](data/ebola_cases.csv): - Reuse one epidemiological parameter to estimate the effective reproductive number for the Ebola dataset. -```{r} -ebola_confirmed <- read_csv("episodes/data/ebola_cases.csv") +```{r,eval=FALSE} +ebola_confirmed <- read_csv("data/raw-data/ebola_cases.csv") ``` ::::::::::::::::: hint @@ -695,9 +695,14 @@ Review the type of distribution of the parameter you choose. ::::::::::::::::: solution +```{r,eval=FALSE} +# read data from tutorial repository R project +ebola_confirmed <- read_csv("episodes/data/ebola_cases.csv") +``` + ```{r,message=FALSE,warning=FALSE,eval=FALSE} # read data -ebola_confirmed <- read_csv("episodes/data/ebola_cases.csv") +ebola_confirmed <- read_csv("data/raw-data/ebola_cases.csv") # list distributions epidist_db(disease = "ebola") %>% From 45db86a32dce98f6935712fcb689ec372cd38f4c Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Wed, 20 Dec 2023 18:56:20 +0000 Subject: [PATCH 086/130] add idea callout to move from lollipop chart to distribution --- episodes/read-delays.Rmd | 27 ++++++++++++++++++++++----- 1 file changed, 22 insertions(+), 5 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index ebf493da..51acb28f 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -41,7 +41,7 @@ pak::pak("epiverse-trace/epiparameter") ## Systematic review of Parameters -The [natural history](../learners/reference.md#naturalhistory) of a infectious disease shows that its development has a regularity from stage to stage. The time periods from a infectious disease inform about the timing of transmission and interventions. +The [natural history](../learners/reference.md#naturalhistory) of an infectious disease shows that its development has a regularity from stage to stage. The time periods from a infectious disease inform about the timing of transmission and interventions. ![Definition of key time periods. From [Xiang et al, 2021](https://www.sciencedirect.com/science/article/pii/S2468042721000038)](fig/time-periods.jpg) @@ -58,9 +58,9 @@ However, early in an epidemic, modelling efforts can be delayed by the lack of a -Let's explore how we can access to time periods and delays using `{epiparameter}` to plug them in to `{EpiNow2}`: +Let's explore how we can access to time periods and delays using `{epiparameter}` to plug them in to `{EpiNow2}`. We'll also use the pipe `%>%` to connect some of their functions, so let's also call to the `{tidyverse}` package: -```{r} +```{r,warning=FALSE,message=FALSE} library(epiparameter) library(EpiNow2) library(tidyverse) @@ -73,14 +73,31 @@ The generation time, jointly with the $R$, can inform about the speed of spread ![From the video by MRC Centre for Global Infectious Disease Analysis, Ep 76. Science In Context - Epi Parameter Review Group with Dr Anne Cori (27-07-2023) at ](fig/reproduction-generation-time.png) -In calculating the effective reproductive number ($Rt$), the generation time distribution is often approximated by the [serial interval](../learners/reference.md#serialinterval) distribution. +In calculating the effective reproductive number ($Rt$), the *generation time* distribution is often approximated by the [serial interval](../learners/reference.md#serialinterval) distribution. This is because the date of onset of symptoms is more easily observed than the date of onset of infectiousness. However, this is valid for diseases in which infectiousness starts after symptom onset [(Chung Lau et al. 2021)](https://academic.oup.com/jid/article/224/10/1664/6356465). In cases where infectiousness starts before symptom onset serial intervals can be negative! This is the case of a pre-symptomatic transmission. -Even if the generation time and serial interval have the same mean, their variance usually differ propagating bias to the Rt estimation. Rt estimates are sensitive not only to the mean generation time but also to the variance and form of the generation interval distribution [(Gostic et al., 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409). +::::::::::::::::: callout + +### From time to probability distributions. + +When calculating the *generation time*, we see that not all primary cases have the same probability to generate a secondary case. The onset and cessation of infectiousness may occur gradually. + +Equally, not all cases have the same probability to have a time period of equal duration. + + + + + + +For that reason, we refer to all time periods them in terms of their distribution parameters of location (like mean and percentiles) and spread + +::::::::::::::::::::::::: + +Also, even if the *generation time* and *serial interval* have the same mean, their variance usually differ propagating bias to the Rt estimation. Rt estimates are sensitive not only to the mean generation time but also to the variance and form of the generation interval distribution [(Gostic et al., 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409). From cd5ee87f3c60afc8ea8e9229bde449da7221ddd5 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Wed, 20 Dec 2023 18:56:37 +0000 Subject: [PATCH 087/130] add one more figure on infectiousness --- learners/reference.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/learners/reference.md b/learners/reference.md index 18718283..72ab7ec4 100644 --- a/learners/reference.md +++ b/learners/reference.md @@ -68,6 +68,8 @@ The relationship between the incubation period and the [serial interval](#seria : Also known as Duration of infectiousness. Time period between the onset and end of infectious [viral shedding](#viralshedding). Viral load and detection of infectious virus are the two key parameters for estimating infectiousness ([Puhach et al., 2022](https://www.nature.com/articles/s41579-022-00822-w)). +![Kinetics of RNA viral loads and infectious virus for ancestral SARS-CoV-2 in patients with mild-to-moderate disease. From [Puhach et al., 2022](https://www.nature.com/articles/s41579-022-00822-w/figures/2)](fig/virus-load-rna-copies.png) + ![Window and kinetics of SARS-CoV-2 infectiousness in recently infected contacts. From [Hakki et al, 2022](https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(22)00226-0/fulltext)](fig/infectiousness-covid19.jpg) From 93c8b4aa202874f506734828d5fa66fcaee40218 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Mon, 18 Dec 2023 10:52:22 +0000 Subject: [PATCH 088/130] add figure tags to url links --- learners/reference.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/learners/reference.md b/learners/reference.md index 72ab7ec4..98da12fa 100644 --- a/learners/reference.md +++ b/learners/reference.md @@ -55,8 +55,8 @@ The generation time distribution is commonly estimated from data on the [serial [Incubation period]{#incubation} : The time between becoming infected and the onset of symptoms. [More information on the incubation period](https://en.wikipedia.org/wiki/Latent_period_(epidemiology)#Incubation_period). -This can be different to the [latent period](#latent) as shown in Figure 4 from ([Xiang et al. (2021)](https://www.sciencedirect.com/science/article/pii/S2468042721000038)). -The relationship between the incubation period and the [serial interval](#serialinterval) helps to define the type of infection transmission (symptomatic or pre-symptomatic). This is shown in Figure 2 at [Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext). +This can be different to the [latent period](#latent) as shown in Figure 4 from ([Xiang et al. (2021)](https://www.sciencedirect.com/science/article/pii/S2468042721000038#fig4)). +The relationship between the incubation period and the [serial interval](#serialinterval) helps to define the type of infection transmission (symptomatic or pre-symptomatic). This is shown in Figure 2 at [Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext#gr2). [Indirect transmission]{#indirect} : Indirectly transmitted infections are passed on to humans via contact with vectors, animals or contaminated environment. Vector-borne infections, zoonoses and water-borne infections are modelled as indirectly transmitted. @@ -79,8 +79,8 @@ The relationship between the incubation period and the [serial interval](#seria ## L [Latent period]{#latent} -: The time between becoming infected and the onset of infectiousness. [More information on the latent period](https://en.wikipedia.org/wiki/Latent_period_(epidemiology)). -This can be different to the [incubation period](#incubation) as shown in Figure 4 from ([Xiang et al, 2021](https://www.sciencedirect.com/science/article/pii/S2468042721000038)) +: The time between becoming infected and the onset of infectiousness. +This can be different to the [incubation period](#incubation) as shown in Figure 4 from ([Xiang et al, 2021](https://www.sciencedirect.com/science/article/pii/S2468042721000038#fig4)) ## M [Model parameters (ODEs)]{#parsode} @@ -120,7 +120,7 @@ This can be different to the [incubation period](#incubation) as shown in Figure : The time delay between the onset of symptoms between a primary case and a secondary case. This can be negative when pre-symptomatic infection occurs. Most commonly, the serial interval distribution of an infection is used to estimate the [generation time](#generationtime) distribution ([(Cori et al., 2017)](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). -The relationship between the serial interval and the [incubation period](#incubation) helps to define the type of infection transmission (symptomatic or pre-symptomatic). This is shown in Figure 2 at [Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext). +The relationship between the serial interval and the [incubation period](#incubation) helps to define the type of infection transmission (symptomatic or pre-symptomatic). This is shown in Figure 2 at [Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext#gr2). [Stochastic model]{#stochastic} : A model that includes some stochastic process resulting in variation in model simulations for the same initial conditions and parameter values. Examples include stochastic differential equations and branching process models. For more detail see [Allen (2017)](https://doi.org/10.1016/j.idm.2017.03.001). From 26aaed497ca457a88b2502ce2cb3794595f40e4d Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 21 Dec 2023 18:01:48 +0000 Subject: [PATCH 089/130] add reference on sampling and censoring bias to si --- learners/reference.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/learners/reference.md b/learners/reference.md index 98da12fa..fcbd4d1a 100644 --- a/learners/reference.md +++ b/learners/reference.md @@ -120,7 +120,7 @@ This can be different to the [incubation period](#incubation) as shown in Figure : The time delay between the onset of symptoms between a primary case and a secondary case. This can be negative when pre-symptomatic infection occurs. Most commonly, the serial interval distribution of an infection is used to estimate the [generation time](#generationtime) distribution ([(Cori et al., 2017)](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). -The relationship between the serial interval and the [incubation period](#incubation) helps to define the type of infection transmission (symptomatic or pre-symptomatic). This is shown in Figure 2 at [Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext#gr2). +The relationship between the serial interval and the [incubation period](#incubation) helps to define the type of infection transmission (symptomatic or pre-symptomatic) ([Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext#gr2)). Different sampling approaches can generate biases given left and right censoring in the estimation of the serial interval ([Chen et al., 2022](https://www.nature.com/articles/s41467-022-35496-8/figures/2)). [Stochastic model]{#stochastic} : A model that includes some stochastic process resulting in variation in model simulations for the same initial conditions and parameter values. Examples include stochastic differential equations and branching process models. For more detail see [Allen (2017)](https://doi.org/10.1016/j.idm.2017.03.001). From 31eb0825632743d1a704f7408e85b1e76ea3d8c0 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 21 Dec 2023 18:06:35 +0000 Subject: [PATCH 090/130] add distributions affected by censoring --- learners/reference.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/learners/reference.md b/learners/reference.md index fcbd4d1a..ff92955d 100644 --- a/learners/reference.md +++ b/learners/reference.md @@ -120,7 +120,7 @@ This can be different to the [incubation period](#incubation) as shown in Figure : The time delay between the onset of symptoms between a primary case and a secondary case. This can be negative when pre-symptomatic infection occurs. Most commonly, the serial interval distribution of an infection is used to estimate the [generation time](#generationtime) distribution ([(Cori et al., 2017)](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). -The relationship between the serial interval and the [incubation period](#incubation) helps to define the type of infection transmission (symptomatic or pre-symptomatic) ([Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext#gr2)). Different sampling approaches can generate biases given left and right censoring in the estimation of the serial interval ([Chen et al., 2022](https://www.nature.com/articles/s41467-022-35496-8/figures/2)). +The relationship between the serial interval and the [incubation period](#incubation) helps to define the type of infection transmission (symptomatic or pre-symptomatic) ([Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext#gr2)). Different sampling approaches can generate biases given left and right censoring in the estimation of the serial interval, also applied to the [infectious period](#infectiousness) and [generation time](#generationtime) estimation ([Chen et al., 2022](https://www.nature.com/articles/s41467-022-35496-8/figures/2)). [Stochastic model]{#stochastic} : A model that includes some stochastic process resulting in variation in model simulations for the same initial conditions and parameter values. Examples include stochastic differential equations and branching process models. For more detail see [Allen (2017)](https://doi.org/10.1016/j.idm.2017.03.001). 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zVpXIw=wvDjF0Zb0uW2&IykA*&qFl8X;FHx#811}gy@urXqmpOo6cYVaj8>XB?f_4J zH`f~B`*9XdRs@N>g1(i-xFxQtx!Q;_g3rvfJe zF=d`^@7R-hRseJ_PEsFf9z_fy@#07lDt-KzdM=(Dp_?ZQUUrcy^A~8%G7UtuYC+L) z&%0(t7_HeN*BVWHOF11NJ?Ok|rCkCN_$5EqI7&hlz5l*5latz< zb5viWqntyinI%4&k!)wKd?73OQBi5vz#~p#K&AM7Ki-@aDOVb_+dh6(=5%i+Ojh09 zY1YD2S_XShxvRUvyZ%j>oH9LCJok%k`*w_nTt%P4-;`IwOIbO2R6pbO0SZokt7_^$ z;cgVc+PHPwGt!3h|dcw5@p^4Q+dvsG>D!h608J>W<94k>cS8Bsp*nRX{c z8ck{LOjgcolA8QRJQXkU - +[Fitted serial interval distribution for (a) COVID-19 and (b) MERS-CoV based on reported transmission pairs in Saudi Arabia. We fitted three commonly used distribution, lognormal, gamma, and Weibull distributions, respectively.](fig/seria-interval-fitted-distributions.jpg) For that reason, we refer to all time periods them in terms of their distribution parameters of location (like mean and percentiles) and spread +| Gamma | mean | shape | rate/scale | +|:--------------|:--------------|:--------------|:--------------| +| MERS-CoV | 14.13(13.9–14.7) | 6.31(4.88–8.52) | 0.43(0.33–0.60) | +| COVID-19 | 5.1(5.0–5.5) | 2.77(2.09–3.88) | 0.53(0.38–0.76) | + +| Weibull | mean | shape | rate/scale | +|:--------------|:--------------|:--------------|:--------------| +| MERS-CoV | 14.2(13.3–15.2) | 3.07(2.64–3.63) | 16.1(15.0–17.1) | +| COVID-19 | 5.2(4.6–5.9) | 1.74(1.46–2.11) | 5.83(5.08–6.67) | + +Table: Serial interval estimates using gamma, Weibull and log normal distributions. 95% confidence intervals for the shape and scale (logmean and sd for log normal) parameters are shown in brackets. + +| Log normal | mean | mean-log | sd-log | +|:--------------|:--------------|:--------------|:--------------| +| MERS-CoV | 14.08(13.1–15.2) | 2.58(2.50–2.68) | 0.44(0.39–0.5) | +| COVID-19 | 5.2(4.2–6.5) | 1.45(1.31–1.61) | 0.63(0.54–0.74) | + ::::::::::::::::::::::::: Also, even if the *generation time* and *serial interval* have the same mean, their variance usually differ propagating bias to the Rt estimation. Rt estimates are sensitive not only to the mean generation time but also to the variance and form of the generation interval distribution [(Gostic et al., 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409). From 7b013c2d607ebc08ff4b722a77e59338f640f085 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 21 Dec 2023 19:11:14 +0000 Subject: [PATCH 092/130] remove episodes out of feature branch --- config.yaml | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/config.yaml b/config.yaml index e3ddcce7..c62ee29f 100644 --- a/config.yaml +++ b/config.yaml @@ -14,7 +14,7 @@ carpentry: 'incubator' title: 'Outbreak analytics with R' # Date the lesson was created (YYYY-MM-DD, this is empty by default) -created: +created: # Comma-separated list of keywords for the lesson keywords: 'forecasts, epidemic models, interventions' @@ -58,13 +58,9 @@ contact: 'andree.valle-campos@lshtm.ac.uk' # - another-learner.md # Order of episodes in your lesson -episodes: +episodes: - introduction.Rmd - read-delays.Rmd -- describe-cases.Rmd -- read-cases.Rmd -- describe-data.Rmd -- create-report.Rmd - quantify-transmissibility.Rmd - create-forecast.Rmd - simulating-transmission.Rmd @@ -73,13 +69,13 @@ episodes: - compare-interventions.Rmd # Information for Learners -learners: +learners: # Information for Instructors -instructors: +instructors: # Learner Profiles -profiles: +profiles: # Customisation --------------------------------------------- # From c8f8874daa1f44939a78fd97cb7e99bb2402802d Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 21 Dec 2023 23:55:19 +0000 Subject: [PATCH 093/130] fix text and references on distrib params --- episodes/read-delays.Rmd | 40 +++++++++++++++++++++------------------- 1 file changed, 21 insertions(+), 19 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index ed77d814..4cf4650b 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -73,27 +73,30 @@ The generation time, jointly with the $R$, can inform about the speed of spread ![From the video by MRC Centre for Global Infectious Disease Analysis, Ep 76. Science In Context - Epi Parameter Review Group with Dr Anne Cori (27-07-2023) at ](fig/reproduction-generation-time.png) -In calculating the effective reproductive number ($Rt$), the *generation time* distribution is often approximated by the [serial interval](../learners/reference.md#serialinterval) distribution. +In calculating the effective reproductive number ($R_{t}$), the *generation time* distribution is often approximated by the [serial interval](../learners/reference.md#serialinterval) distribution. This is because the date of onset of symptoms is more easily observed than the date of onset of infectiousness. - +![A schematic of the relationship of different time periods of transmission between an infector and an infectee in a transmission pair. Exposure window is defined as the time interval having viral exposure, and transmission window is defined as the time interval for onward transmission with respect to the infection time ([Chung Lau et al. 2021](https://academic.oup.com/jid/article/224/10/1664/6356465)).](fig/serial-interval-observed.jpeg) -However, this is valid for diseases in which infectiousness starts after symptom onset [(Chung Lau et al. 2021)](https://academic.oup.com/jid/article/224/10/1664/6356465). In cases where infectiousness starts before symptom onset serial intervals can be negative! This is the case of a pre-symptomatic transmission. +However, this is mostly valid for diseases in which infectiousness starts after symptom onset ([Chung Lau et al. 2021](https://academic.oup.com/jid/article/224/10/1664/6356465)). In cases where infectiousness starts before symptom onset, the serial intervals can be negative, which is the case of a pre-symptomatic transmission ([Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext#gr2)). + +Additionally, even if the *generation time* and *serial interval* have the same mean, their variance usually differ propagating bias to the $R_{t}$ estimation. $R_{t}$ estimates are sensitive not only to the mean generation time but also to the variance and form of the generation interval distribution [(Gostic et al., 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409). ::::::::::::::::: callout -### From time to probability distributions. +### From time periods to probability distributions. -When calculating the *generation time*, we see that not all primary cases have the same probability to generate a secondary case. The onset and cessation of infectiousness may occur gradually. +When we calculate the *serial interval*, we see that not all case pairs have the same time length. We also observe this variability for individual time periods like the [incubation period](../learners/reference.md#incubation) and [infectious period](../learners/reference.md#infectiousness). -Equally, not all cases have the same probability to have a time period of equal duration. +![Serial intervals of possible case pairs in (a) COVID-19 and (b) MERSCoV. Pairs represent a presumed infector and their presumed infectee plotted by date of symptom onset ([Althobaity et al., 2022](https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig6)).](fig/serial-interval-pairs.jpg) -[Serial intervals of possible case pairs in (a) COVID-19 and (b) MERSCoV. Pairs represent a presumed infector and their presumed infectee plotted by date of symptom onset.](fig/serial-interval-pairs.jpg) +To summarize these data from individual and pair time periods, we can find the **statistical distributions** that best fits to the data ([McFarland et al., 2023](https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2023.28.27.2200806)). + -[Fitted serial interval distribution for (a) COVID-19 and (b) MERS-CoV based on reported transmission pairs in Saudi Arabia. We fitted three commonly used distribution, lognormal, gamma, and Weibull distributions, respectively.](fig/seria-interval-fitted-distributions.jpg) +![Fitted serial interval distribution for (a) COVID-19 and (b) MERS-CoV based on reported transmission pairs in Saudi Arabia. We fitted three commonly used distribution, lognormal, gamma, and Weibull distributions, respectively ([Althobaity et al., 2022](https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig6)).](fig/seria-interval-fitted-distributions.jpg) -For that reason, we refer to all time periods them in terms of their distribution parameters of location (like mean and percentiles) and spread +Statistical distributions are summarized in terms of their **summary statistics** like the *location* (mean and percentiles) and *spread* (variance or standard deviation) of the distribution, or with their **distribution parameters** that inform about the *form* (shape and rate/scale) of the distribution. Each of these values can be reported with the *uncertainty* (95% confidence intervals) of each estimate. | Gamma | mean | shape | rate/scale | |:--------------|:--------------|:--------------|:--------------| @@ -105,22 +108,16 @@ For that reason, we refer to all time periods them in terms of their distributio | MERS-CoV | 14.2(13.3–15.2) | 3.07(2.64–3.63) | 16.1(15.0–17.1) | | COVID-19 | 5.2(4.6–5.9) | 1.74(1.46–2.11) | 5.83(5.08–6.67) | -Table: Serial interval estimates using gamma, Weibull and log normal distributions. 95% confidence intervals for the shape and scale (logmean and sd for log normal) parameters are shown in brackets. - | Log normal | mean | mean-log | sd-log | |:--------------|:--------------|:--------------|:--------------| | MERS-CoV | 14.08(13.1–15.2) | 2.58(2.50–2.68) | 0.44(0.39–0.5) | | COVID-19 | 5.2(4.2–6.5) | 1.45(1.31–1.61) | 0.63(0.54–0.74) | -::::::::::::::::::::::::: +Table: Serial interval estimates using gamma, Weibull and log normal distributions. 95% confidence intervals for the shape and scale (logmean and sd for log normal) parameters are shown in brackets ([Althobaity et al., 2022](https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig6)). -Also, even if the *generation time* and *serial interval* have the same mean, their variance usually differ propagating bias to the Rt estimation. Rt estimates are sensitive not only to the mean generation time but also to the variance and form of the generation interval distribution [(Gostic et al., 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409). + - - - - - +::::::::::::::::::::::::: ::::::::::::::::::::::::::::::::: challenge @@ -153,9 +150,14 @@ Why do you conclude that? :::::::::::::::::::::::::: - ::::::::::::::::::::::::::::::::::::::::::: + + + + + + ## Extract epidemiological parameters Our goal now is to replace the `generation_time` input that we used for `EpiNow2::epinow()`. 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b/learners/reference.md @@ -44,6 +44,9 @@ title: 'Glossary of Terms: Epiverse-TRACE' [Generation time]{#generationtime} : Time between the onset of infectiousness of an index case and its secondary case. This always needs to be positive. The generation time distribution is commonly estimated from data on the [serial interval](#serialinterval) distribution of an infection ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). +Different sampling approaches can generate biases given left and right censoring in the estimation of the serial interval that can propagate bias to the estimation of the [incubation period](#incubation) and generation time ([Chen et al., 2022](https://www.nature.com/articles/s41467-022-35496-8/figures/2)). + + [Growth rate]{#growth} : The exponential growth rate tells us how much cases are increasing or decreasing at the start of an epidemic. It gives us a measure of speed of transmission, see [Dushoff & Park, 2021](https://royalsocietypublishing.org/doi/full/10.1098/rspb.2020.1556). @@ -56,7 +59,7 @@ The generation time distribution is commonly estimated from data on the [serial : The time between becoming infected and the onset of symptoms. [More information on the incubation period](https://en.wikipedia.org/wiki/Latent_period_(epidemiology)#Incubation_period). This can be different to the [latent period](#latent) as shown in Figure 4 from ([Xiang et al. (2021)](https://www.sciencedirect.com/science/article/pii/S2468042721000038#fig4)). -The relationship between the incubation period and the [serial interval](#serialinterval) helps to define the type of infection transmission (symptomatic or pre-symptomatic). This is shown in Figure 2 at [Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext#gr2). +The relationship between the incubation period and the [serial interval](#serialinterval) helps to define the type of infection transmission (symptomatic or pre-symptomatic) ([Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext#gr2)). [Indirect transmission]{#indirect} : Indirectly transmitted infections are passed on to humans via contact with vectors, animals or contaminated environment. Vector-borne infections, zoonoses and water-borne infections are modelled as indirectly transmitted. @@ -120,7 +123,7 @@ This can be different to the [incubation period](#incubation) as shown in Figure : The time delay between the onset of symptoms between a primary case and a secondary case. This can be negative when pre-symptomatic infection occurs. Most commonly, the serial interval distribution of an infection is used to estimate the [generation time](#generationtime) distribution ([(Cori et al., 2017)](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). -The relationship between the serial interval and the [incubation period](#incubation) helps to define the type of infection transmission (symptomatic or pre-symptomatic) ([Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext#gr2)). Different sampling approaches can generate biases given left and right censoring in the estimation of the serial interval, also applied to the [infectious period](#infectiousness) and [generation time](#generationtime) estimation ([Chen et al., 2022](https://www.nature.com/articles/s41467-022-35496-8/figures/2)). +The relationship between the serial interval and the [incubation period](#incubation) helps to define the type of infection transmission (symptomatic or pre-symptomatic) ([Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext#gr2)). [Stochastic model]{#stochastic} : A model that includes some stochastic process resulting in variation in model simulations for the same initial conditions and parameter values. Examples include stochastic differential equations and branching process models. For more detail see [Allen (2017)](https://doi.org/10.1016/j.idm.2017.03.001). From a60d9bebc1d0653d0bf381148133165b23033c21 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 21 Dec 2023 23:59:41 +0000 Subject: [PATCH 096/130] remove figures from infectious period --- learners/reference.md | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/learners/reference.md b/learners/reference.md index d5de3ef8..9b6e41c0 100644 --- a/learners/reference.md +++ b/learners/reference.md @@ -67,13 +67,9 @@ The relationship between the incubation period and the [serial interval](#serial [Initial conditions]{#initial} : In [ODEs](#ordinary), the initial conditions are the values of the state variables at the start of the model simulation (at time 0). For example, if there is one infectious individual in a population of 1000 in an Susceptible-Infectious-Recovered model, the initial conditions would be $S(0) = 999$, $I(0) = 1$, $R(0) = 0$. -[Infectiousness]{#infectiousness} -: Also known as Duration of infectiousness. Time period between the onset and end of infectious [viral shedding](#viralshedding). Viral load and detection of infectious virus are the two key parameters for estimating infectiousness ([Puhach et al., 2022](https://www.nature.com/articles/s41579-022-00822-w)). - - -![Kinetics of RNA viral loads and infectious virus for ancestral SARS-CoV-2 in patients with mild-to-moderate disease. From [Puhach et al., 2022](https://www.nature.com/articles/s41579-022-00822-w/figures/2)](fig/virus-load-rna-copies.png) - -![Window and kinetics of SARS-CoV-2 infectiousness in recently infected contacts. From [Hakki et al, 2022](https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(22)00226-0/fulltext)](fig/infectiousness-covid19.jpg) +[Infectious period]{#infectiousness} +: Also known as Duration of infectiousness. Time period between the onset and end of infectious [viral shedding](#viralshedding). +Viral load and detection of infectious virus are the two key parameters for estimating infectiousness ([Puhach et al., 2022](https://www.nature.com/articles/s41579-022-00822-w) and [Hakki et al, 2022](https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(22)00226-0/fulltext)](fig/infectiousness-covid19.jpg)). From fba4e0e2ecf70b1fa531f48dacaccf048eca1b85 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 22 Dec 2023 12:14:44 +0000 Subject: [PATCH 097/130] edit text with grammarly --- episodes/read-delays.Rmd | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 4cf4650b..78b7d22f 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -41,7 +41,7 @@ pak::pak("epiverse-trace/epiparameter") ## Systematic review of Parameters -The [natural history](../learners/reference.md#naturalhistory) of an infectious disease shows that its development has a regularity from stage to stage. The time periods from a infectious disease inform about the timing of transmission and interventions. +The [natural history](../learners/reference.md#naturalhistory) of an infectious disease shows that its development has a regularity from stage to stage. The time periods from an infectious disease inform about the timing of transmission and interventions. ![Definition of key time periods. From [Xiang et al, 2021](https://www.sciencedirect.com/science/article/pii/S2468042721000038)](fig/time-periods.jpg) @@ -50,15 +50,15 @@ The [natural history](../learners/reference.md#naturalhistory) of an infectious ### Definitions -Take a look at the [glossary](../learners/reference.md) for the definitions of all the time periods of the figure above! +Look at the [glossary](../learners/reference.md) for the definitions of all the time periods of the figure above! ::::::::::::::::::::::::: -However, early in an epidemic, modelling efforts can be delayed by the lack of a centralized resource that summarized input parameters for the disease of interest ([Nash et al., 2023](https://mrc-ide.github.io/epireview/)). Projects like `{epiparameter}` and `{epireview}` are building online catalogs that can help building models faster for coming outbreaks and epidemics from known and unknown pathogens related to known families of viruses. +However, early in an epidemic, modelling efforts can be delayed by the lack of a centralized resource that summarises input parameters for the disease of interest ([Nash et al., 2023](https://mrc-ide.github.io/epireview/)). Projects like `{epiparameter}` and `{epireview}` are building online catalogues that can help build models faster for coming outbreaks and epidemics from known and unknown pathogens related to known families of viruses. -Let's explore how we can access to time periods and delays using `{epiparameter}` to plug them in to `{EpiNow2}`. We'll also use the pipe `%>%` to connect some of their functions, so let's also call to the `{tidyverse}` package: +Let's explore how we can access time periods and delays using `{epiparameter}` to plug them into `{EpiNow2}`. We'll also use the pipe `%>%` to connect some of their functions, so let's also call to the `{tidyverse}` package: ```{r,warning=FALSE,message=FALSE} library(epiparameter) @@ -71,16 +71,16 @@ library(tidyverse) The generation time, jointly with the $R$, can inform about the speed of spread and its feasibility of control. Given a $R>1$, with a shorter generation time, cases can appear more quickly. -![From the video by MRC Centre for Global Infectious Disease Analysis, Ep 76. Science In Context - Epi Parameter Review Group with Dr Anne Cori (27-07-2023) at ](fig/reproduction-generation-time.png) +![Video from the MRC Centre for Global Infectious Disease Analysis, Ep 76. Science In Context - Epi Parameter Review Group with Dr Anne Cori (27-07-2023) at ](fig/reproduction-generation-time.png) In calculating the effective reproductive number ($R_{t}$), the *generation time* distribution is often approximated by the [serial interval](../learners/reference.md#serialinterval) distribution. -This is because the date of onset of symptoms is more easily observed than the date of onset of infectiousness. +This is because it is easier to observe and measure the onset of symptoms than the onset of infectiousness. ![A schematic of the relationship of different time periods of transmission between an infector and an infectee in a transmission pair. Exposure window is defined as the time interval having viral exposure, and transmission window is defined as the time interval for onward transmission with respect to the infection time ([Chung Lau et al. 2021](https://academic.oup.com/jid/article/224/10/1664/6356465)).](fig/serial-interval-observed.jpeg) -However, this is mostly valid for diseases in which infectiousness starts after symptom onset ([Chung Lau et al. 2021](https://academic.oup.com/jid/article/224/10/1664/6356465)). In cases where infectiousness starts before symptom onset, the serial intervals can be negative, which is the case of a pre-symptomatic transmission ([Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext#gr2)). +However, using the *serial interval* as an approximation of the *generation time* is primarily valid for diseases in which infectiousness starts after symptom onset ([Chung Lau et al. 2021](https://academic.oup.com/jid/article/224/10/1664/6356465)). In cases where infectiousness starts before symptom onset, the serial intervals can have negative values, which is the case of a pre-symptomatic transmission ([Nishiura et al. (2020)](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext#gr2)). -Additionally, even if the *generation time* and *serial interval* have the same mean, their variance usually differ propagating bias to the $R_{t}$ estimation. $R_{t}$ estimates are sensitive not only to the mean generation time but also to the variance and form of the generation interval distribution [(Gostic et al., 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409). +Additionally, even if the *generation time* and *serial interval* have the same mean, their variance usually differs, propagating bias to the $R_{t}$ estimation. $R_{t}$ estimates are sensitive not only to the mean generation time but also to the variance and form of the generation interval distribution [(Gostic et al., 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409). ::::::::::::::::: callout From 749c27035bc6daecb8e12d0f334963a45b24e447 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 22 Dec 2023 12:22:11 +0000 Subject: [PATCH 098/130] fix links to figures and tables in distribution presentation --- episodes/read-delays.Rmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 78b7d22f..3a62fb31 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -94,9 +94,9 @@ To summarize these data from individual and pair time periods, we can find the * -![Fitted serial interval distribution for (a) COVID-19 and (b) MERS-CoV based on reported transmission pairs in Saudi Arabia. We fitted three commonly used distribution, lognormal, gamma, and Weibull distributions, respectively ([Althobaity et al., 2022](https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig6)).](fig/seria-interval-fitted-distributions.jpg) +![Fitted serial interval distribution for (a) COVID-19 and (b) MERS-CoV based on reported transmission pairs in Saudi Arabia. We fitted three commonly used distribution, lognormal, gamma, and Weibull distributions, respectively ([Althobaity et al., 2022](https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig5)).](fig/seria-interval-fitted-distributions.jpg) -Statistical distributions are summarized in terms of their **summary statistics** like the *location* (mean and percentiles) and *spread* (variance or standard deviation) of the distribution, or with their **distribution parameters** that inform about the *form* (shape and rate/scale) of the distribution. Each of these values can be reported with the *uncertainty* (95% confidence intervals) of each estimate. +Statistical distributions are summarized in terms of their **summary statistics** like the *location* (mean and percentiles) and *spread* (variance or standard deviation) of the distribution, or with their **distribution parameters** that inform about the *form* (shape and rate/scale) of the distribution. Each of these estimated values can be reported with their **uncertainty** (95% confidence intervals). | Gamma | mean | shape | rate/scale | |:--------------|:--------------|:--------------|:--------------| @@ -113,7 +113,7 @@ Statistical distributions are summarized in terms of their **summary statistics* | MERS-CoV | 14.08(13.1–15.2) | 2.58(2.50–2.68) | 0.44(0.39–0.5) | | COVID-19 | 5.2(4.2–6.5) | 1.45(1.31–1.61) | 0.63(0.54–0.74) | -Table: Serial interval estimates using gamma, Weibull and log normal distributions. 95% confidence intervals for the shape and scale (logmean and sd for log normal) parameters are shown in brackets ([Althobaity et al., 2022](https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig6)). +Table: Serial interval estimates using Gamma, Weibull and Log normal distributions. 95% confidence intervals for the shape and scale (logmean and sd for log normal) parameters are shown in brackets ([Althobaity et al., 2022](https://www.sciencedirect.com/science/article/pii/S2468042722000537#tbl3)). From c6ae5b5127faee2b9f1492b727c58b6d883b6396 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 22 Dec 2023 13:10:38 +0000 Subject: [PATCH 099/130] add presymptomatic line and reference --- episodes/read-delays.Rmd | 2 ++ 1 file changed, 2 insertions(+) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 3a62fb31..4ec4f7a2 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -791,6 +791,8 @@ The proportion of pre-symptomatic transmission contributes to the effective repr ![Parameter estimates. Plausible ranges for the key parameters R0 and θ (read main text for sources) for four viral infections of public concern are shown as shaded regions. The size of the shaded area reflects the uncertainties in the parameter estimates. [Fraser et al., 2004](https://www.pnas.org/doi/10.1073/pnas.0307506101)](fig/reproduction-number-pre-symptomatic.png) +The proportion of presymptomatic transmission in SARS-CoV-2 has been heterogeneous with point estimates of 40% or grater ([Buitrago-Garcia et al., 2022](https://journals.plos.org/plosmedicine/article/figure?id=10.1371/journal.pmed.1003987.g003)) + ::::::::::::::::::::::::::::::::: challenge ### WHAT TYPE OF TRANSMISSION? From 22940341fad117389faf97425ac3b5b15b92307f Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 22 Dec 2023 13:11:04 +0000 Subject: [PATCH 100/130] edit challenge writing --- episodes/read-delays.Rmd | 20 +++++++++----------- 1 file changed, 9 insertions(+), 11 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 4ec4f7a2..d34fd1f5 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -115,26 +115,24 @@ Statistical distributions are summarized in terms of their **summary statistics* Table: Serial interval estimates using Gamma, Weibull and Log normal distributions. 95% confidence intervals for the shape and scale (logmean and sd for log normal) parameters are shown in brackets ([Althobaity et al., 2022](https://www.sciencedirect.com/science/article/pii/S2468042722000537#tbl3)). - - ::::::::::::::::::::::::: ::::::::::::::::::::::::::::::::: challenge ### Serial interval -Let's assume that COVID-19 and SARS has similar Reproduction number values, and the Serial interval a good proxy of the generation interval. +Assume that COVID-19 and SARS have similar reproduction number values and that the serial interval is a fair approximation of the generation time. Given the Serial interval of both infections in the figure below: - Which one would be harder to control? - Why do you conclude that? -![Serial interval of novel coronavirus (COVID-19) infections overlaid with a published distribution of SARS. From [Nishiura et al, 2020](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext)](fig/serial-interval-covid-sars.jpg) +![Serial interval of novel coronavirus (COVID-19) infections overlaid with a published distribution of SARS. ([Nishiura et al, 2020](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext))](fig/serial-interval-covid-sars.jpg) ::::::::::::::::: hint -The peak of each curve can inform you about the location of the mean of each distribution. The larger the mean, the larger the serial interval. +The peak of each curve can inform you about the location of the mean of each distribution. The larger the mean, the larger the serial interval. :::::::::::::::::::::: @@ -146,17 +144,17 @@ Which one would be harder to control? Why do you conclude that? -- COVID-19 has the lowest mean serial interval. The approximate mean value for the serial interval of COVID-19 is around 4 days, and SARS is around 7 days. Thus, COVID-19 is likely to have newer generations in less time than SARS, assuming similar reproduction number in both. +- COVID-19 has the lowest mean serial interval. The approximate mean value for the serial interval of COVID-19 is around four days, and SARS is about seven days. Thus, COVID-19 will likely have newer generations in less time than SARS, assuming similar reproduction numbers in both. :::::::::::::::::::::::::: ::::::::::::::::::::::::::::::::::::::::::: - - - - - +:::::::::::::::::::::: instructor + +The objective of the assessment above is to assess on the interpretation of a larger or shorter generation time. + +:::::::::::::::::::::: ## Extract epidemiological parameters From e0cec384a3bb8c0a0f5c1015c4f53054aa2de57b Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 22 Dec 2023 18:14:14 +0000 Subject: [PATCH 101/130] remove installation code from episodes --- episodes/introduction.Rmd | 9 --------- episodes/read-delays.Rmd | 7 ------- 2 files changed, 16 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 23dd35a7..c5434d83 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -32,15 +32,6 @@ This episode requires you to be familiar with: **Epidemic theory** : Reproduction number. -This also requires the following package: - -```r -install_version( - package = "EpiNow2", version = "1.4.0", - repos = "http://cran.us.r-project.org" -) -``` - ::::::::::::::::::::::::::::::::: ## Why to use R packages for Outbreak analytics? diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index d34fd1f5..886b3eb4 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -30,13 +30,6 @@ This episode requires you to be familiar with: **Epidemic theory** : Epidemiological parameters. Time periods. -This also requires the following package: - -```r -if(!require("pak")) install.packages("pak") -pak::pak("epiverse-trace/epiparameter") -``` - ::::::::::::::::::::::::::::::::: ## Systematic review of Parameters From 12aca304b6eb04ee4fced13f489e52f63b18a7b8 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 22 Dec 2023 20:29:20 +0000 Subject: [PATCH 102/130] replace ver to express calculation --- episodes/introduction.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index c5434d83..cb78dbdf 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -44,7 +44,7 @@ of interest ([Prism, 2016](http://prism.edu.au/publications/prism-modeling-guide - if $R>1$, the epidemic is likely to growth, - if $R<1$, the epidemic is likely to decline. -To estimate the reproduction number we can use two __data inputs__: the incidence of reported cases and the [generation time](../learners/reference.md#generationtime) distribution. Then, to get it, we need to apply the appropriate mathematical models written in code with the required computational methods. That is not enough! Following _good practices_, that code should be peer-reviewed and contain internal tests to double-check that we are getting the estimates we expect. Imagine rewriting all of it during a health emergency! +To estimate the reproduction number we can use two __data inputs__: the incidence of reported cases and the [generation time](../learners/reference.md#generationtime) distribution. But to calculate it, we need to apply the appropriate mathematical models written in code with the required computational methods. That is not enough! Following _good practices_, the code we write should be peer-reviewed and contain internal tests to double-check that we are getting the estimates we expect. Imagine rewriting all of it during a health emergency! In R, the fundamental unit of shareable code is the _package_. A package bundles together code, data, documentation, and tests, and is easy to share with others ([Wickham and Bryan, 2023](https://r-pkgs.org/introduction.html)). We, as epidemiologists, can contribute to their collaborative maintenance as a community, to perform less error-prone data analysis pipelines. From 1a608c44998c4be07360bf5176e7f4d90182d502 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 22 Dec 2023 20:29:39 +0000 Subject: [PATCH 103/130] add delay word for generation time --- episodes/introduction.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index cb78dbdf..7f530652 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -97,14 +97,14 @@ generation_time <- dist_spec( In the `epinow()` function we can add: - the `reported_cases` data frame, -- the `generation_time` distribution, and +- the `generation_time` delay distribution, and - the computation `stan` parameters for this calculation: ```{r,message=FALSE,warning=FALSE,cache=TRUE} epinow_estimates <- epinow( # cases reported_cases = example_confirmed[1:60], - # distribution + # delays generation_time = generation_time_opts(generation_time), # computation stan = stan_opts( From fff8ee5504eb19e3ddcfd2b99613a93082544e7d Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 22 Dec 2023 20:39:16 +0000 Subject: [PATCH 104/130] review intro with grammarly --- episodes/introduction.Rmd | 42 +++++++++++++++++++-------------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 7f530652..d83cd76a 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -8,15 +8,15 @@ editor_options: :::::::::::::::::::::::::::::::::::::: questions -- Why to use R packages for Outbreak analytics? +- Why use R packages for Outbreak analytics? - What can we do to analyse our outbreak data? -- How can I start doing Outbreak analytics with R? +- How can I start doing Outbreak Analytics with R? :::::::::::::::::::::::::::::::::::::::::::::::: ::::::::::::::::::::::::::::::::::::: objectives -- Explain our vision on the need of outbreak analytics R packages. +- Explain our vision on the need for outbreak analytics R packages. - Share our strategy to create R packages into an outbreak analytics pipeline. - Define our plan to start your learning path in outbreak analytics with R. @@ -36,27 +36,27 @@ This episode requires you to be familiar with: ## Why to use R packages for Outbreak analytics? -Outbreaks appear with different diseases and in different contexts but what all of them have in common are the key public health questions ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371#d1e605)). +Outbreaks appear with different diseases and in different contexts, but what all of them have in common are the key public health questions ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371#d1e605)). -Is the epidemic going to take off? Is it under control? How much effort will be needed to control it? We can answer to them by _quantifying the transmissibility_ of the disease. The most used parameter for this is the reproduction number ($R$), the average number of secondary infections caused by a typical primary case in the population +Is the epidemic going to take off? Is it under control? How much effort will be needed to control it? We can answer them by _quantifying the transmissibility_ of the disease. The most used parameter for this is the reproduction number ($R$), the average number of secondary infections caused by a typical primary case in the population of interest ([Prism, 2016](http://prism.edu.au/publications/prism-modeling-guideline/)). We can intuitively interpret it as: -- if $R>1$, the epidemic is likely to growth, +- if $R>1$, the epidemic is likely to grow, - if $R<1$, the epidemic is likely to decline. -To estimate the reproduction number we can use two __data inputs__: the incidence of reported cases and the [generation time](../learners/reference.md#generationtime) distribution. But to calculate it, we need to apply the appropriate mathematical models written in code with the required computational methods. That is not enough! Following _good practices_, the code we write should be peer-reviewed and contain internal tests to double-check that we are getting the estimates we expect. Imagine rewriting all of it during a health emergency! +We can estimate the reproduction number by initially using two __data inputs__: the incidence of reported cases and the [generation time](../learners/reference.md#generationtime) distribution. But to calculate it, we must apply the appropriate mathematical models written in code with the required computational methods. That is not enough! Following _good practices_, the code we write should be peer-reviewed and contain internal tests to double-check that we are getting the estimates we expect. Imagine rewriting all of it during a health emergency! -In R, the fundamental unit of shareable code is the _package_. A package bundles together code, data, documentation, and tests, and is easy to share with others ([Wickham and Bryan, 2023](https://r-pkgs.org/introduction.html)). We, as epidemiologists, can contribute to their collaborative maintenance as a community, to perform less error-prone data analysis pipelines. +In R, the fundamental unit of shareable code is the _package_. A package bundles together code, data, documentation, and tests and is easy to share with others ([Wickham and Bryan, 2023](https://r-pkgs.org/introduction.html)). We, as epidemiologists, can contribute to their collaborative maintenance as a community to perform less error-prone data analysis pipelines. ::::::::::::::::::::::::::::::::: discussion ### Questions to think about -Remember your last experience with outbreak data and reflect in these questions: +Remember your last experience with outbreak data and reflect on these questions: -- What data sources you needed to understand the outbreak? -- How did you get accessed to that data? -- Is that analysis pipeline you followed reusable for a next response? +- What data sources did you need to understand the outbreak? +- How did you get access to that data? +- Is that analysis pipeline you followed reusable for the next response? Reflect on your experiences. @@ -65,7 +65,7 @@ Reflect on your experiences. ## Example: Quantify transmission -The `{EpiNow2}` package provide us with a three-steps solution to _quantify the transmissibility_. Let's see how to do this with a minimal example. First, load the package: +The `{EpiNow2}` package provides a three-step solution to _quantify the transmissibility_. Let's see how to do this with a minimal example. First, load the package: ```{r,warning=FALSE} library(EpiNow2) @@ -73,7 +73,7 @@ library(EpiNow2) ### First, get your case data -Case incidence data needs be stored in a data frame with the observed number of cases per day. We can read example from the package: +Case incidence data must be stored in a data frame with the observed number of cases per day. We can read an example from the package: ```{r} example_confirmed @@ -81,7 +81,7 @@ example_confirmed ### Then, set the generation time -Not all primary cases have the same probability to generate a secondary case. The onset and cessation of [infectiousness](../learners/reference.md#infectiousness) may occur gradually. For `{EpiNow2}`, we can specify it as a probability `distribution` with `mean`, standard deviation `sd`, and maximum value `max`: +Not all primary cases have the same probability of generating a secondary case. The onset and cessation of [infectiousness](../learners/reference.md#infectiousness) may occur gradually. For `{EpiNow2}`, we can specify it as a probability `distribution` with `mean`, standard deviation `sd`, and maximum value `max`: ```{r} generation_time <- dist_spec( @@ -134,7 +134,7 @@ However, _quantifying the transmissibility_ during a real-life outbreak response Usually, we receive outbreak data in non-standard formats, requiring specific steps and taking the most time to prepare usable data inputs. Some of them are: -- Read parameters from literature +- Read delay distributions from the literature - Read and clean case data - Validate your linelist - Describe case data @@ -152,9 +152,9 @@ So, all these tasks can be interconnected in a pipeline: ## What can we do? -Our strategy is to gradually incorporate specialized R packages into our traditional analysis pipeline. These packages should fill the gaps in these epidemiology-specific tasks in response to outbreaks. +Our strategy is gradually incorporating specialised R packages into our traditional analysis pipeline. These packages should fill the gaps in these epidemiology-specific tasks in response to outbreaks. -Epiverse-TRACE aim is to provide a software ecosystem for outbreak analytics. We support the development of software pieces, make the existing ones interoperable for the user experience, and stimulate a community of practice. +Epiverse-TRACE's aim is to provide a software ecosystem for outbreak analytics. We support the development of software pieces, make the existing ones interoperable for the user experience, and stimulate a community of practice. ![](fig/pkgs-hexlogos.png) @@ -186,7 +186,7 @@ file.path(pkg_path) |> Our plan for these tutorials is to introduce key solutions from packages in all the tasks before and after the _Quantify transmission_ task, plus the required theory concepts to interpret modelling outputs and make rigorous conclusions. -- In the first set of episodes, you will learn how to optimize the reading and cleaning of epidemiological parameters and case data to input them into the _Quantify transmission_ task. These preliminary tasks are the __Early tasks__. This includes package like `{readepi}`, `{cleanepi}`, `{linelist}`, `{epiparameter}`, and `{episoap}`. +- In the first set of episodes, you will learn how to optimise the reading of delay distributions and cleaning of case data to input them into the _Quantify transmission_ task. These preliminary tasks are the __Early tasks__. These include packages like `{readepi}`, `{cleanepi}`, `{linelist}`, `{epiparameter}`, and `{episoap}`. - Then, we will get deeper into the packages and required theory to _Quantify transmission_ and perform more real-time analysis tasks next to it. These are the __Middle tasks__. This includes `{EpiNow2}`, `{cfr}`, `{epichains}`, and `{superspreading}`. @@ -196,8 +196,8 @@ Our plan for these tutorials is to introduce key solutions from packages in all ::::::::::::::::::::::::::::::::::::: keypoints - Our vision is to have pipelines of R packages for outbreak analytics. -- Our strategy is to create interconnected tasks to get public health relevant outputs. -- Our plan is to introduce about package solutions and theory bits for each of the tasks in the outbreak analytics pipeline. +- Our strategy is to create interconnected tasks to get relevant outputs for public health questions. +- We plan to introduce package solutions and theory bits for each of the tasks in the outbreak analytics pipeline. :::::::::::::::::::::::::::::::::::::::::::::::: From 4df864433b2524e8c213206c9cf9e80364ca903e Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 23 Dec 2023 00:09:24 +0000 Subject: [PATCH 105/130] add specific objectives and keypoints --- episodes/read-delays.Rmd | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 886b3eb4..fccb0f74 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -15,8 +15,11 @@ editor_options: ::::::::::::::::::::::::::::::::::::: objectives -- Get epidemological parameters from systematic reviews. -- Convert statistical summary to distribution parameters. +- Get delays from a systematic review with `{epiparameter}`. +- Get statistical summaries and distribution parameters of delay distributions. +- Use distribution functions from delay distributions. +- Convert a continuous to a discrete delay distribution. +- Convert delay statistical summaries to distribution parameters. :::::::::::::::::::::::::::::::::::::::::::::::: @@ -1096,7 +1099,11 @@ update it from last epiparameter test ::::::::::::::::::::::::::::::::::::: keypoints -- Use `{epiparameter}` +- Use `{epiparameter}` to access to systematic review catalog of epidemiological delay distributions. +- Use `epidist_db()` to select single delay distributions. +- Use `list_distributions()` for a overview of multiple delay distributions. +- Use `discretise()` to convert continuous to discrete delay distributions. +- Use `{epiparameter}` probability functions for any delay distributions. :::::::::::::::::::::::::::::::::::::::::::::::: From 6aed597ba2490c5c5005a719569507f673fbea64 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 23 Dec 2023 00:10:09 +0000 Subject: [PATCH 106/130] homogenize reference to delays --- episodes/read-delays.Rmd | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index fccb0f74..3377fff0 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -751,10 +751,14 @@ serial_interval_ebola <- distribution = "gamma" ) +# name of type of distribution +# only for the discretised distribution +ebola_serial_discrete$prob_dist$name + epinow_estimates <- epinow( # cases reported_cases = ebola_confirmed, - # distribution + # delays generation_time = generation_time_opts(serial_interval_ebola), # computation stan = stan_opts( @@ -860,9 +864,8 @@ Use `{epiparameter}` to: epinow_estimates <- epinow( # cases reported_cases = example_confirmed[1:60], - # generation time - generation_time = generation_time_opts(serial_interval_covid), # delays + generation_time = generation_time_opts(serial_interval_covid), delays = delay_opts(incubation_time_covid), # computation stan = stan_opts( @@ -898,7 +901,7 @@ incubation_time_covid <- dist_spec( epinow_estimates <- epinow( # cases reported_cases = example_confirmed[1:60], - # generation time + # delays generation_time = generation_time_opts(serial_interval_covid), # delays delays = delay_opts(incubation_time_covid), @@ -978,7 +981,7 @@ influenza_cleaned <- outbreaks::influenza_england_1978_school %>% epinow_estimates <- epinow( # cases reported_cases = influenza_cleaned, - # distribution + # delays generation_time = generation_time_opts(generation_time_influenza), # computation stan = stan_opts( From f26c5ba65a537bdc052997b82cee5527a1376a8b Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 23 Dec 2023 00:10:48 +0000 Subject: [PATCH 107/130] complete introduction section --- episodes/read-delays.Rmd | 36 +++++++++++++++++++++++++++++++----- 1 file changed, 31 insertions(+), 5 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 3377fff0..1d005b99 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -35,7 +35,7 @@ This episode requires you to be familiar with: ::::::::::::::::::::::::::::::::: -## Systematic review of Parameters +## Introduction The [natural history](../learners/reference.md#naturalhistory) of an infectious disease shows that its development has a regularity from stage to stage. The time periods from an infectious disease inform about the timing of transmission and interventions. @@ -50,11 +50,38 @@ Look at the [glossary](../learners/reference.md) for the definitions of all the ::::::::::::::::::::::::: -However, early in an epidemic, modelling efforts can be delayed by the lack of a centralized resource that summarises input parameters for the disease of interest ([Nash et al., 2023](https://mrc-ide.github.io/epireview/)). Projects like `{epiparameter}` and `{epireview}` are building online catalogues that can help build models faster for coming outbreaks and epidemics from known and unknown pathogens related to known families of viruses. +However, early in an epidemic, modelling efforts can be delayed by the lack of a centralized resource that summarises input parameters for the disease of interest ([Nash et al., 2023](https://mrc-ide.github.io/epireview/)). Projects like `{epiparameter}` and `{epireview}` are building online catalogues following systematic review protocols that can help build models faster for coming outbreaks and epidemics from known pathogens and unknown ones related to known families of viruses. - + -Let's explore how we can access time periods and delays using `{epiparameter}` to plug them into `{EpiNow2}`. We'll also use the pipe `%>%` to connect some of their functions, so let's also call to the `{tidyverse}` package: +To exemplify how to use `{epiparameter}` in your analysis pipeline, our goal in this episode will be to replace the `generation_time` input that we can use for `EpiNow2::epinow()`. + +```r +epinow_estimates <- epinow( + # cases + reported_cases = example_confirmed[1:60], + # delays + generation_time = generation_time_opts(generation_time), + # computation + stan = stan_opts( + cores = 4, samples = 1000, chains = 3, + control = list(adapt_delta = 0.99) + ) +) +``` + +To do this replacement, instead of plug-in numeric values to `EpiNow2::dist_spec()` to manually specify the delay distribution parameters, we are going to collect them from the library of epidemiological parameters provided by `{epiparameter}`: + +```r +generation_time <- dist_spec( + mean = 3.6, + sd = 3.1, + max = 20, + distribution = "lognormal" +) +``` + +Let's explore how we can access to this and other time delays using `{epiparameter}`. We'll use the pipe `%>%` to connect some of their functions, so let's also call to the `{tidyverse}` package: ```{r,warning=FALSE,message=FALSE} library(epiparameter) @@ -62,7 +89,6 @@ library(EpiNow2) library(tidyverse) ``` - ## Find a Generation time The generation time, jointly with the $R$, can inform about the speed of spread and its feasibility of control. Given a $R>1$, with a shorter generation time, cases can appear more quickly. From 716491cf6156824248b046c10aacf4ce7cf573b9 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 23 Dec 2023 00:11:25 +0000 Subject: [PATCH 108/130] relocate episode goal to introduction --- episodes/read-delays.Rmd | 27 --------------------------- 1 file changed, 27 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 1d005b99..918be9da 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -180,33 +180,6 @@ The objective of the assessment above is to assess on the interpretation of a la ## Extract epidemiological parameters -Our goal now is to replace the `generation_time` input that we used for `EpiNow2::epinow()`. - -```r -epinow_estimates <- epinow( - # cases - reported_cases = example_confirmed[1:60], - # distribution - generation_time = generation_time_opts(generation_time), - # computation - stan = stan_opts( - cores = 4, samples = 1000, chains = 3, - control = list(adapt_delta = 0.99) - ) -) -``` - -To do this replacement, instead of plug-in numeric values to `EpiNow2::dist_spec()` to manually specify the distribution parameters, we are going to collect them from the library of epidemiological parameters provided by `{epiparameter}`: - -```r -generation_time <- dist_spec( - mean = 3.6, - sd = 3.1, - max = 20, - distribution = "lognormal" -) -``` - First, let's assume that the data set `example_confirmed` have COVID-19 observed cases. So, we need to find a reported generation time for COVID-19 or any other useful parameter for this aim. Let's start by looking at how many parameters do we have in the epidemiological distributions database (`epidist_db`) for the `disease` named `covid`-19: From 1c17adf9c4becb9cf807789a5fe202e13d6bc808 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 23 Dec 2023 00:12:34 +0000 Subject: [PATCH 109/130] complete content writing --- episodes/read-delays.Rmd | 124 ++++++++++++++++++++++++--------------- 1 file changed, 77 insertions(+), 47 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 918be9da..5c10d555 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -108,7 +108,7 @@ Additionally, even if the *generation time* and *serial interval* have the same ### From time periods to probability distributions. -When we calculate the *serial interval*, we see that not all case pairs have the same time length. We also observe this variability for individual time periods like the [incubation period](../learners/reference.md#incubation) and [infectious period](../learners/reference.md#infectiousness). +When we calculate the *serial interval*, we see that not all case pairs have the same time length. We will observe this variability for any case pair and individual time period, including the [incubation period](../learners/reference.md#incubation) and [infectious period](../learners/reference.md#infectiousness). ![Serial intervals of possible case pairs in (a) COVID-19 and (b) MERSCoV. Pairs represent a presumed infector and their presumed infectee plotted by date of symptom onset ([Althobaity et al., 2022](https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig6)).](fig/serial-interval-pairs.jpg) @@ -200,7 +200,7 @@ epiparameter::epidist_db( ) ``` -Currently, in the library of epidemiological parameters we have one `generation` time entry but for Influenza. We can try to look at the `serial` intervals for `COVID`-19. +Currently, in the library of epidemiological parameters we have one `generation` time entry but for Influenza. Taking the considerations explained above, we can try to look at the `serial` intervals for `COVID`-19. ```{r} epiparameter::epidist_db( @@ -255,9 +255,13 @@ epiparameter::epidist_db( ### How does `single_epidist` works? -Take a look into help documentation for `?epiparameter::epidist_db()`. +Looking at the help documentation for `?epiparameter::epidist_db()`: -> Note: If multiple entries match the arguments supplied and `single_epidist = TRUE` then the `⁠⁠ that is parameterised and has the largest sample size will be returned (read `?is_parameterised()`). If multiple entries are equal after this sorting the first entry will be returned. +- If multiple entries match the arguments supplied and `single_epidist = TRUE`, +- Then the `⁠`⁠ that is parameterised and has the largest sample size will be returned. +- If multiple entries are equal after this sorting the first entry will be returned. + +What does a parametrised `` is? Look at `?is_parameterised`. ::::::::::::::::::::::::: @@ -350,7 +354,10 @@ Take 1 minute to: 3. Explore all the other nested elements within the `` object. -Share what is the most interesting element for you! +Share about: + +- What elements you find useful for your analysis? +- What other elements would you like to see in this object? How? ::::::::: hint @@ -388,7 +395,7 @@ We will explore these concepts at the end! ## Continuous distributions -The following output has four entries with different content in the probability distribution (`prob_distribution`) column: +The following output has four entries with different content in the **probability distribution** (`prob_distribution`) column: ```{r} distribution <- @@ -401,22 +408,27 @@ distribution %>% list_distributions() ``` -Entries with a missing value (``) in the `prob_distribution` column only posses summary statistics associated with it. Run these commands to find the difference: +Entries with a missing value (``) in the `prob_distribution` column are *non-parameterised* entries. They have summary statistics but no probability distribution. Compare these two outputs: ```{r,eval=FALSE} distribution[[1]]$summary_stats distribution[[1]]$prob_dist ``` -As detailed in `?is_parameterised`, a parameterised distribution is the entry that has a probability distribution associated to it provided by an `inference_method` as shown in `metadata`. Run these commands to find the difference: +::::::::::::::::: spoiler + +### Parameterised entries have an Inference method + +As detailed in `?is_parameterised`, a parameterised distribution is the entry that has a probability distribution associated to it provided by an `inference_method` as shown in `metadata`: ```{r,eval=FALSE} distribution[[1]]$metadata$inference_method distribution[[2]]$metadata$inference_method -distribution[[3]]$metadata$inference_method distribution[[4]]$metadata$inference_method ``` +::::::::::::::::::::::::: + In the `epiparameter::list_distributions()` output, we can also find different types of probability distributions (e.g., log-normal, weibull, normal). ```{r} @@ -430,10 +442,14 @@ In `{epiparameter}` you will mostly find **continuous** distributions like these - the *Cumulative Distribution Function (CDF)*. ```{r} -plot(distribution[[2]], day_range = 0:20) +plot(distribution[[2]]) ``` -With the `day_range` argument you can change the length or number of days in the `x` axis. +With the `day_range` argument you can change the length or number of days in the `x` axis. Explore how does it looks like: + +```{r,eval=FALSE} +plot(distribution[[2]], day_range = 0:20) +``` ::::::::::::::::: discussion @@ -441,34 +457,32 @@ With the `day_range` argument you can change the length or number of days in the Explore this shinyapp called **The Distribution Zoo**! -Follow these steps to reproduce the `covid_serialint` distribution: +Follow these steps to reproduce the form of the `covid_serialint` distribution: -- Access to shinyapp website! +- Access to shinyapp website, - Go to the left panel, - Keep the *Category of distribution*: `Continuous Univariate`, -- Select a new *Distribution type*: `Log-Normal`, -- Move the **sliders**, i.e. the graphical control element that allows you to adjust a value by moving a handle along a track or bar, to the `covid_serialint` parameters. - - +- Select a new *Type of distribution*: `Log-Normal`, +- Move the **sliders**, i.e. the graphical control element that allows you to adjust a value by moving a handle along a horizontal track or bar, to the `covid_serialint` parameters. -Replicate these with the `distribution` object and all its list elements: `2`, `3`, and `4`. Explore: +Replicate these with the `distribution` object and all its list elements: `2`, `3`, and `4`. Explore How does the shape of a distribution change when its parameters change. -- How does the shape of a distribution change when its parameters change? +Share about: -Share what other useful features you find in the website. +- What other features in the website you find useful? ::::::::::::::::::::::::: + + ## Distribution functions -In R, all the statistical distributions has functions to access to: +In R, all the statistical distributions has functions to access to the: -- density, -- cumulative distribution function, -- quantile function, and -- random variate generation. +- Probability **Density** function (PDF), +- Cumulative Distribution function (**CDF**), +- **Quantile** function, and +- **Random** drawn values from the given distribution. :::::::::::: spoiler @@ -516,9 +530,13 @@ Access to the reference documentation (Help files) for these functions is access ::::::::::::::::::::::::::::::::: challenge -### 99th percentile of the serial interval? +### Lenght of contact tracing and the Serial interval + +The **serial interval** is important in the optimization of contact tracing since it provides a time window for the containment of a disease spread ([Fine, 2003](https://academic.oup.com/aje/article/158/11/1039/162725)). + +Calculate: -When considering secondary cases, after how many days following the symptom onset of primary cases can we expect 99% of symptom onset to occur? +- When considering secondary cases, after how many days following the symptom onset of primary cases can we expect 99% of symptom onset to occur? ::::::::::::::::: hint @@ -534,7 +552,7 @@ quantile(covid_serialint, p = 0.99) The 99% percent of the symptom onset of secondary cases will happen after 14.99 days after the symptom onset of primary cases. -_Does this result make sense or is expected?_ +_Is this result expected in epidemiological terms?_ :::::::::::::::::::::::::: @@ -553,7 +571,8 @@ We can use the set of distribution functions for a _continuous_ distribution (as When we `epiparameter::discretise()` the continuous distribution we get a **discrete**(-ized) distribution: ```{r} -covid_serialint_discrete <- epiparameter::discretise(covid_serialint) +covid_serialint_discrete <- + epiparameter::discretise(covid_serialint) covid_serialint_discrete ``` @@ -564,29 +583,32 @@ We identify this change in the `Distribution:` output line of the `` ob Distribution: discrete lnorm ``` -For a **continuous** distribution, we plot the *Probability Density Function (PDF)*: +While for a **continuous** distribution, we plot the *Probability Density Function (PDF)*, for a **discrete** distribution, we plot the *Probability Mass Function (PMF)*: ```{r,eval=FALSE} +# continuous plot(covid_serialint) -``` - -For a **discrete** distribution, we plot the *Probability Mass Function (PMF)*: -```{r} +# discrete plot(covid_serialint_discrete) ``` To finally get a `max` value let's access to the quantile value of the 99.9th percentile or `0.999` probability of the distribution with the `prob_dist$q` notation, similarly to how we access to the `summary_stats` values. ```{r} -covid_serialint_discrete_max <- covid_serialint_discrete$prob_dist$q(p = 0.999) +covid_serialint_discrete_max <- + covid_serialint_discrete$prob_dist$q(p = 0.999) ``` ::::::::::::::::::::::::::::::::: challenge -### 99th percentile of the incubation period of COVID-19 +### Lenght of quarentine and Incubation period -Within what time frame do 99% of individuals who develop COVID-19 symptoms exhibit them after infection? +The **incubation period** distribution is a useful delay to assess for the length of active monitoring or quarantine ([Lauer et al., 2020](https://www.acpjournals.org/doi/10.7326/M20-0504)). + +Calculate: + +- Within what time frame do 99% of individuals who develop COVID-19 symptoms exhibit them after infection? ::::::::::::::::: hint @@ -699,11 +721,12 @@ base::plot(epinow_estimates) ::::::::::::::::::::::::::::::::: challenge -### EBOLA +### Ebola's effective reproductive number Download the [Ebola dataset](data/ebola_cases.csv): - Reuse one epidemiological parameter to estimate the effective reproductive number for the Ebola dataset. +- Why you choose that parameter? ```{r,eval=FALSE} ebola_confirmed <- read_csv("data/raw-data/ebola_cases.csv") @@ -711,14 +734,17 @@ ebola_confirmed <- read_csv("data/raw-data/ebola_cases.csv") ::::::::::::::::: hint -Key functions we applied until now: +To calculate the Rt we need: + +- data set with confirmed cases per day, and +- one key delay distribution -- `epidist_db` -- `list_distributions` -- `discretise` -- probability functions +Key functions we applied in this episodes are: -Review the type of distribution of the parameter you choose. +- `epidist_db()` +- `list_distributions()` +- `discretise()` +- probability functions for continuous and discrete distributions :::::::::::::::::::::: @@ -738,7 +764,11 @@ epidist_db(disease = "ebola") %>% list_distributions() # subset one distribution -ebola_serial <- epidist_db(disease = "ebola", epi_dist = "serial", single_epidist = T) +ebola_serial <- epidist_db( + disease = "ebola", + epi_dist = "serial", + single_epidist = T +) ebola_serial_discrete <- discretise(ebola_serial) From 188866d22dd49e9aa2b5f8023d5f1e0e731a4c88 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 23 Dec 2023 05:26:31 +0000 Subject: [PATCH 110/130] separate linelist --- episodes/introduction.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index d83cd76a..440a5f6e 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -136,7 +136,7 @@ Usually, we receive outbreak data in non-standard formats, requiring specific st - Read delay distributions from the literature - Read and clean case data -- Validate your linelist +- Validate your line list - Describe case data And this is not the end. After _quantifying transmissibility_ we need to answer more key public health questions like: What is the attack rate we expect? What would be the impact of a given intervention? We can use the reproduction number and other outputs as new inputs for complementary tasks. For example: @@ -162,7 +162,7 @@ Epiverse-TRACE's aim is to provide a software ecosystem for outbreak analytics. # eval = TRUE # turn on if maintainers manage to # adapt css from main page to varnish -pkg_list <- c("readepi","cleanepi","linelist","epiparameter","episoap") +# pkg_list <- c("readepi","cleanepi","linelist","epiparameter","episoap") # from https://github.com/epiverse-trace/epiverse-trace.github.io/blob/main/index.qmd From 0ec7536661b07aaf46702fdeb6c39cc56eb78912 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 23 Dec 2023 05:26:53 +0000 Subject: [PATCH 111/130] complete editing of episode --- episodes/read-delays.Rmd | 447 ++++++++++++++++++++++++--------------- 1 file changed, 280 insertions(+), 167 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 5c10d555..7e1401ee 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -8,8 +8,9 @@ editor_options: :::::::::::::::::::::::::::::::::::::: questions -- how to get delay distributions from a systematic review? -- what is the natural history of the disease? +- How to get delay distributions from a systematic review? +- How to connect reused delays with my existing analysis pipeline? +- When to reuse delays from a systematic review? :::::::::::::::::::::::::::::::::::::::::::::::: @@ -19,7 +20,6 @@ editor_options: - Get statistical summaries and distribution parameters of delay distributions. - Use distribution functions from delay distributions. - Convert a continuous to a discrete delay distribution. -- Convert delay statistical summaries to distribution parameters. :::::::::::::::::::::::::::::::::::::::::::::::: @@ -95,7 +95,7 @@ The generation time, jointly with the $R$, can inform about the speed of spread ![Video from the MRC Centre for Global Infectious Disease Analysis, Ep 76. Science In Context - Epi Parameter Review Group with Dr Anne Cori (27-07-2023) at ](fig/reproduction-generation-time.png) -In calculating the effective reproductive number ($R_{t}$), the *generation time* distribution is often approximated by the [serial interval](../learners/reference.md#serialinterval) distribution. +In calculating the effective reproduction number ($R_{t}$), the *generation time* distribution is often approximated by the [serial interval](../learners/reference.md#serialinterval) distribution. This is because it is easier to observe and measure the onset of symptoms than the onset of infectiousness. ![A schematic of the relationship of different time periods of transmission between an infector and an infectee in a transmission pair. Exposure window is defined as the time interval having viral exposure, and transmission window is defined as the time interval for onward transmission with respect to the infection time ([Chung Lau et al. 2021](https://academic.oup.com/jid/article/224/10/1664/6356465)).](fig/serial-interval-observed.jpeg) @@ -330,7 +330,6 @@ Now, from the output of `epiparameter::epidist_db()`, What is an [offspring dist :::::::::::::::::::::::::: - ::::::::::::::::::::::::::::::::::::::::::: ## Extract the summary statistics @@ -530,13 +529,13 @@ Access to the reference documentation (Help files) for these functions is access ::::::::::::::::::::::::::::::::: challenge -### Lenght of contact tracing and the Serial interval +### Window for contact tracing and the Serial interval -The **serial interval** is important in the optimization of contact tracing since it provides a time window for the containment of a disease spread ([Fine, 2003](https://academic.oup.com/aje/article/158/11/1039/162725)). +The **serial interval** is important in the optimization of contact tracing since it provides a time window for the containment of a disease spread ([Fine, 2003](https://academic.oup.com/aje/article/158/11/1039/162725)). Depending on the serial interval, we can evaluate the need to expand the number of days pre-onset to consider in the contact tracing to include more backwards contacts ([Davis et al., 2020](https://assets.publishing.service.gov.uk/media/61e9ab3f8fa8f50597fb3078/S0523_Oxford_-_Backwards_contact_tracing.pdf)). -Calculate: +With the COVID-19 serial interval (`covid_serialint`) calculate: -- When considering secondary cases, after how many days following the symptom onset of primary cases can we expect 99% of symptom onset to occur? +- How much more of the backward cases could be captured if the contact tracing method considered contacts up to 6 days pre-onset compared to 2 days pre-onset? ::::::::::::::::: hint @@ -547,12 +546,34 @@ In Figure 5 from the [R probability functions for the normal distribution](https ::::::::::::::::: solution ```{r} -quantile(covid_serialint, p = 0.99) +plot(covid_serialint) +cdf(covid_serialint,q = 2) +cdf(covid_serialint,q = 6) ``` -The 99% percent of the symptom onset of secondary cases will happen after 14.99 days after the symptom onset of primary cases. +Given the COVID-19 serial interval: -_Is this result expected in epidemiological terms?_ +- A contact tracing method that consider contacts up to 2 days pre-onset will already capture around 11.1% of backwards cases. + +- If this period was extended to 6 days pre-onset then this could include 76.2% of backward contacts. + +:::::::::::::::::::::::::: + +::::::::::::::::: solution + +### What if + +If we exchange the question between days and cumulative probability to: + +- When considering secondary cases, after how many days following the symptom onset of primary cases can we expect 55% of symptom onset to occur? + +```{r} +quantile(covid_serialint, p = 0.55) +``` + +An interpretation could be: + +- The 55% percent of the symptom onset of secondary cases will happen after 4.2 days after the symptom onset of primary cases. :::::::::::::::::::::::::: @@ -602,17 +623,17 @@ covid_serialint_discrete_max <- ::::::::::::::::::::::::::::::::: challenge -### Lenght of quarentine and Incubation period +### Lenght of quarantine and Incubation period -The **incubation period** distribution is a useful delay to assess for the length of active monitoring or quarantine ([Lauer et al., 2020](https://www.acpjournals.org/doi/10.7326/M20-0504)). +The **incubation period** distribution is a useful delay to assess for the length of active monitoring or quarantine ([Lauer et al., 2020](https://www.acpjournals.org/doi/10.7326/M20-0504)). Similarly, delays from symptom onset to recovery (or death) will determine the required duration of health-care and case isolation ([Cori et al., 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). Calculate: -- Within what time frame do 99% of individuals who develop COVID-19 symptoms exhibit them after infection? +- Within what exact time frame do 99% of individuals who develop COVID-19 symptoms exhibit them after infection? ::::::::::::::::: hint -First, we are asking about a different distribution delay: incubation period +What is the delay distribution that measures the time between infection and symptoms onset? Probability function for `` **discrete** distributions differ from the *continuous* ones! @@ -670,13 +691,56 @@ covid_incubation_discrete$prob_dist$q(0.99) 99% of those who develop COVID-19 symptoms will do so within 16 days of infection. -_Dive into the structure of this new `` R object for discrete distributions!_ +_Is this result expected in epidemiological terms?_ :::::::::::::::::::::::::: +::::::::::::::::::::::::::::::::::::::::::: + + +::::::::::::::::::::::::::::::::: challenge + +### Ebola's severity parameter + +A severity parameter like the duration of hospitalization could add into the information needed about the bed capacity in response to an outbreak ([Cori et al., 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). + +For Ebola: + +- what is a reported point estimate and uncertainty of the mean duration of health-care and case isolation? + +::::::::::::::::: hint + +An informative delay measures the time from symptom onset to recovery or death. + +:::::::::::::::::::::: + +::::::::::::::::: solution + +```{r} +# one way to get the list of all the available parameters +epidist_db(disease = "all") %>% + list_distributions() %>% + as_tibble() %>% + distinct(epi_distribution) + +ebola_severity <- epidist_db( + disease = "ebola", + epi_dist = "onset to discharge" +) + +# point estimate +ebola_severity$summary_stats$mean +# 95% confidence intervals +ebola_severity$summary_stats$mean_ci +# limits of the confidence intervals +ebola_severity$summary_stats$mean_ci_limits +``` + +:::::::::::::::::::::::::: ::::::::::::::::::::::::::::::::::::::::::: + ## Plug-in `{epiparameter}` to `{EpiNow2}` Now we are ready to plug-in all to the `EpiNow2::dist_spec()` function! @@ -697,7 +761,7 @@ serial_interval_covid ### Warning -Using the serial interval instead of the generation time is an alternative that can propagate bias in your estimates. Even more in diseases with reported pre-symptomatic transmission! +Using the serial interval instead of the generation time is an alternative that can propagate bias in your estimates. Even more in diseases with reported pre-symptomatic transmission. :::::::::::::::::: @@ -721,11 +785,11 @@ base::plot(epinow_estimates) ::::::::::::::::::::::::::::::::: challenge -### Ebola's effective reproductive number +### Ebola's effective reproduction number Download the [Ebola dataset](data/ebola_cases.csv): -- Reuse one epidemiological parameter to estimate the effective reproductive number for the Ebola dataset. +- Reuse one epidemiological parameter to estimate the effective reproduction number for the Ebola dataset. - Why you choose that parameter? ```{r,eval=FALSE} @@ -805,78 +869,17 @@ plot(epinow_estimates) ::::::::::::::::::::::::::::::::::::::::::: +## Adjusting for reporting delays -## Add the Incubation period - -For `{EpiNow2}`, we can specify relevant delay distributions like the [incubation period](../learners/reference.md#incubation). - -The lengths of the Serial interval and Incubation period determine the type of transmission of a disease. - -![The relationship between the incubation period and serial interval. From [Nishiura 2020](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext)](fig/incubation-period-serial-interval.jpg) - -The proportion of pre-symptomatic transmission contributes to the effective reproductive number and expected new cases to have. - -![Parameter estimates. Plausible ranges for the key parameters R0 and θ (read main text for sources) for four viral infections of public concern are shown as shaded regions. The size of the shaded area reflects the uncertainties in the parameter estimates. [Fraser et al., 2004](https://www.pnas.org/doi/10.1073/pnas.0307506101)](fig/reproduction-number-pre-symptomatic.png) - -The proportion of presymptomatic transmission in SARS-CoV-2 has been heterogeneous with point estimates of 40% or grater ([Buitrago-Garcia et al., 2022](https://journals.plos.org/plosmedicine/article/figure?id=10.1371/journal.pmed.1003987.g003)) - -::::::::::::::::::::::::::::::::: challenge - -### WHAT TYPE OF TRANSMISSION? - -Compare the serial interval and incubation period of Influenza and MERS: - -- What type of transmission have Influenza? -- What type of transmission have MERS? -- Does these results correlates with the available evidence? - -::::::::::::::::: hint - -Key functions: - -- `epidist_db()` -- `epidist$summary_stats$` - -:::::::::::::::::::::: - -::::::::::::::::: solution - -```{r} -# pre-symptomatic transmission -epidist_db(disease = "influenza",epi_dist = "incubation",single_epidist = T)$summary_stats$mean -epidist_db(disease = "influenza",epi_dist = "serial",single_epidist = T)$summary_stats$mean - -# symptomatic transmission -epidist_db(disease = "mers",epi_dist = "incubation",single_epidist = T)$summary_stats$median -epidist_db(disease = "mers",epi_dist = "serial",single_epidist = T)$summary_stats$mean -``` - -```{r,eval=FALSE} -# pre-symptomatic transmission -epidist_db(disease = "covid",epi_dist = "incubation",author = "Stephen",single_epidist = T)$summary_stats$mean -epidist_db(disease = "covid",epi_dist = "serial",author = "Nishiura",single_epidist = T)$summary_stats$mean - -# symptomatic transmission -epidist_db(disease = "ebola",epi_dist = "incubation",single_epidist = T)$summary_stats$mean -epidist_db(disease = "ebola",epi_dist = "serial",single_epidist = T)$summary_stats$mean -``` - - -:::::::::::::::::::::::::: - - -::::::::::::::::::::::::::::::::::::::::::: +Estimating $R_t$ requires data on the daily number of new infections. Due to lags in the development of detectable viral loads, symptom onset, seeking care, and reporting, these numbers are not readily available. All observations reflect transmission events from some time in the past. In other words, if $d$ is the delay from infection to observation, then observations at time $t$ inform $R_{t−d}$, not $R_t$. [(Gostic et al., 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409#sec007) +![Timeline for chain of disease reporting, the Netherlands. Lab, laboratory; PHA, public health authority. From [Marinović et al., 2015](https://wwwnc.cdc.gov/eid/article/21/2/13-0504_article)](fig/disease-reporting.jpg) - +The delay distribution could be inferred jointly with the underlying times of infection or estimated as the sum of the [incubation period](../learners/reference.md#incubation) distribution and the distribution of delays from symptom onset to observation from line list data ([reporting delay](../learners/reference.md#reportingdelay)). - - +For `{EpiNow2}`, we can specify these complementary delay distributions in the `delays` argument. - - - - +![Rt is a measure of transmission at time t. Observations after time t must be adjusted. ICU, intensive care unit. From [Gostic et al., 2020](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409#sec007)](fig/rt-adjusting-delays.png) ::::::::::::::::::::::::::::::::: challenge @@ -884,11 +887,13 @@ epidist_db(disease = "ebola",epi_dist = "serial",single_epidist = T)$summary_sta Use `{epiparameter}` to: -- Find and incubation period for COVID-19 -- Add to our last `epinow()` code chunk the `incubation_time_covid` in the `delays` argument. +- Find and incubation period for COVID-19. +- Add it our last `epinow()` code chunk using the `delays` argument and the `delay_opts()` helper function. ::::::::::::::::: hint +The `delays` argument and the `delay_opts()` helper function are analogous to the `generation_time` argument and the `generation_time_opts()` helper function. + ```r epinow_estimates <- epinow( # cases @@ -946,11 +951,7 @@ base::plot(epinow_estimates) :::::::::::::::::::::::::: - -::::::::::::::::::::::::::::::::::::::::::: - - -:::::::::::::: discussion +:::::::::::::: solution ### How much it changed? @@ -958,15 +959,121 @@ After adding the incubation period, discuss: - Does the retrospective trend of forecast changed? - Does the uncertainty changed? +- How would you explain or interpret any of these changes? :::::::::::::::::::::::::::: -## Extract parameters +::::::::::::::::::::::::::::::::::::::::::: - +::::::::::::::::::::::::::::::::: challenge + +### Ebola's effective reproduction number adjusted by reporting delays + +Using the same [Ebola dataset](data/ebola_cases.csv): + +- Reuse one additional epidemiological parameter for the `delays` argument in `EpiNow2::epinow()`. +- Estimate the effective reproduction number using `EpiNow2::epinow()`. +- Why you choose that parameter? + +```{r,eval=FALSE} +ebola_confirmed <- read_csv("data/raw-data/ebola_cases.csv") +``` + +::::::::::::::::: hint + +There are two complementary delay distributions that we can use to estimate the $R_t$ at time $t$. + +:::::::::::::::::::::: + +::::::::::::::::: solution + +```{r,eval=FALSE} +# read data from tutorial repository R project +ebola_confirmed <- read_csv("episodes/data/ebola_cases.csv") +``` + +```{r,message=FALSE,warning=FALSE,eval=FALSE} +# read data +ebola_confirmed <- read_csv("data/raw-data/ebola_cases.csv") + +# list distributions +epidist_db(disease = "ebola") %>% + list_distributions() + +# subset one distribution for the generation time +ebola_serial <- epidist_db( + disease = "ebola", + epi_dist = "serial", + single_epidist = T +) + +ebola_serial_discrete <- discretise(ebola_serial) + +serial_interval_ebola <- + dist_spec( + mean = ebola_serial$summary_stats$mean, + sd = ebola_serial$summary_stats$sd, + max = ebola_serial_discrete$prob_dist$q(p = 0.999), + distribution = "gamma" + ) + +# subset one distribution for delay of incubation period +ebola_incubation <- epidist_db( + disease = "ebola", + epi_dist = "incubation", + single_epidist = T +) + +ebola_incubation_discrete <- discretise(ebola_incubation) + +incubation_period_ebola <- + dist_spec( + mean = ebola_incubation$summary_stats$mean, + sd = ebola_incubation$summary_stats$sd, + max = ebola_incubation_discrete$prob_dist$q(p = 0.999), + distribution = "gamma" + ) + +epinow_estimates <- epinow( + # cases + reported_cases = ebola_confirmed, + # delays + generation_time = generation_time_opts(serial_interval_ebola), + delays = delay_opts(incubation_period_ebola), + # computation + stan = stan_opts( + cores = 4, samples = 1000, chains = 3, + control = list(adapt_delta = 0.99) + ) +) + +plot(epinow_estimates) + +``` + +:::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::::::::: + + +::::::::::::::::::::::::::::::::: challenge + +### Extract parameters + +Use the `influenza_england_1978_school` from the `{outbreaks}` package to calculate the effective reproduction number. + +::::::::::::::::: hint + +How to get the mean and standard deviation from a generation time with median and quantiles as summary statistics? + +- Look at how to extract parameters from `{epiparameter}` vignette on [parameter extraction and conversion](https://epiverse-trace.github.io/epiparameter/articles/extract_convert.html) + +:::::::::::::::::::::: + +::::::::::::::::: solution -### formative + ```{r,eval=FALSE} # what parameters are are available for influenza? @@ -979,15 +1086,14 @@ influenza_generation <- epidist_db(disease = "influenza",epi_dist = "generation" influenza_generation_discrete <- discretise(influenza_generation) +# problem +# the summary statistics fo not have mean and sd +influenza_generation$summary_stats influenza_generation$summary_stats$median influenza_generation$summary_stats$quantiles -# question: is dispersion valid for quantiles? -# convert_summary_stats_to_params( -# distribution = "lnorm", -# median = influenza_generation$summary_stats$median, -# quantiles = influenza_generation$summary_stats$quantiles) - +# solution +# extract parameters from percentiles influenza_extracted <- extract_param( type = "percentiles", values = c(influenza_generation$summary_stats$quantiles[1], @@ -996,6 +1102,8 @@ influenza_extracted <- extract_param( percentiles = c(0.05,0.95) ) +influenza_extracted + generation_time_influenza <- dist_spec( mean = influenza_extracted[1], @@ -1022,106 +1130,111 @@ epinow_estimates <- epinow( plot(epinow_estimates) ``` -### formative +```{r,eval=FALSE,echo=FALSE} +# question: is dispersion valid for quantiles? +# convert_summary_stats_to_params( +# distribution = "lnorm", +# median = influenza_generation$summary_stats$median, +# quantiles = influenza_generation$summary_stats$quantiles) +``` + +:::::::::::::::::::::::::: -```{r} -# formative, now with ebola ----------------------------------------------- +::::::::::::::::::::::::::::::::::::::::::: -# read ebola +## When to reuse? When to estimate? +In the early stage of an outbreak we can rely on reusing parameters, for known and unknown pathogens, even for the [Disease X](https://en.wikipedia.org/wiki/Disease_X), a pathogen currently unknown to cause human disease and potentially cause a serious international epidemic ([WHO, 2018](https://www.who.int/activities/prioritizing-diseases-for-research-and-development-in-emergency-contexts)). +But when data from **lines list paired with contact tracing** is available, we are in the possibility to estimate the key delays distributions that best fit our data. This will help us to inform, compare and update any previous estimate about questions like: + +- How to get serial interval from case data? +- how long should contacts be followed? +- what is the required duration of contact tracing? +- how long should cases be isolated to reduce transmission? + +However, the methods to accurately estimate delays like the generation interval from contact tracing data involve adjusting for biases like like **[censoring](../learners/reference.md#censoring)**, **[right truncation](../learners/reference.md#truncation)** and **[epidemic phase bias](../learners/reference.md#phasebias)**. [(Gostic et al., 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409) + +We can identify what entries in the `{epiparameter}` library assessed for these biases in their methodology with the `method_assess` nested entry: + +```{r} +covid_serialint$method_assess ``` -However, this still propagates potential biases like +::::::::::::::::: testimonial -- censoring bias (short periods) -- reporting bias (recent contacts) +### How to estimating delay distributions for Disease X? -## Add the Reporting delays +Refer to this excellent tutorial on how to estimate the serial interval and incubation period of Disease X accounting for *censoring* using Bayesian inference with packages like `{rstan}` and `{coarseDataTools}`. -Estimating Rt requires data on the daily number of new infections (i.e., transmission events). Due to lags in the development of detectable viral loads, symptom onset, seeking care, and reporting, these numbers are not readily available. All observations reflect transmission events from some time in the past. In other words, if d is the delay from infection to observation, then observations at time t inform Rt−d, not Rt. [(Gostic et al., 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409#sec007) +- Tutuorial in English: +- Tutorial en Español: -![Rt is a measure of transmission at time t. Observations after time t must be adjusted. ICU, intensive care unit. From [Gostic et al., 2020](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409#sec007)](fig/rt-adjusting-delays.png) +::::::::::::::::::::::::::::: -We can opt to adjust `{EpiNow2}` outputs by the [reporting delay](../learners/reference.md#reportingdelay) that condition the report of confirmed cases. +:::::::::::::::::::::::::: spoiler -![Timeline for chain of disease reporting, the Netherlands. Lab, laboratory; PHA, public health authority. From [Marinović et al., 2015](https://wwwnc.cdc.gov/eid/article/21/2/13-0504_article)](fig/disease-reporting.jpg) +### How to assess for pre-symptomatic infection? -### any epiparameter? +The lengths of the Serial interval and Incubation period determine the type of transmission of a disease. -```{r} -epidist_db(disease = "all") %>% list_distributions() %>% - as_tibble() %>% - distinct(epi_distribution) -``` +![The relationship between the incubation period and serial interval. From [Nishiura 2020](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext)](fig/incubation-period-serial-interval.jpg) -### replace in epinow2 +Estimating the proportion of presymptomatic infections, or the extent to which infectiousness precedes symptom onset will determine the effectiveness of contact tracing and the feasibility of controlling an outbreak ([Fraser et al., 2004](https://www.pnas.org/doi/full/10.1073/pnas.0307506101) and [Hellewell et al., 2020](https://www.thelancet.com/article/S2214-109X(20)30074-7/fulltext)). -```{r} + -``` +![Parameter estimates. Plausible ranges for the key parameters R0 and θ (read main text for sources) for four viral infections of public concern are shown as shaded regions. The size of the shaded area reflects the uncertainties in the parameter estimates. [Fraser et al., 2004](https://www.pnas.org/doi/10.1073/pnas.0307506101)](fig/reproduction-number-pre-symptomatic.png) -### epinow2 +Meta-analysis on the proportion of presymptomatic and asymptomatic transmission in SARS-CoV-2 found limitations of the evidence given high heterogeneity and high risk of selection and information bias between studies ([Buitrago-Garcia et al., 2022](https://journals.plos.org/plosmedicine/article/figure?id=10.1371/journal.pmed.1003987.g003)). This is a call to action to improve the Outbreak Analytic pipelines to use and reuse in the early phase of an outbreak. -```{r,message=FALSE,warning=FALSE,cache=TRUE,eval=FALSE} -reporting_delay <- dist_spec( - mean = convert_to_logmean(2, 1), - sd = convert_to_logsd(2, 1), - max = 10, distribution = "lognormal" -) +::::::::::::::::::::::::::::::::::: -epinow_estimates <- epinow( - # cases - reported_cases = example_confirmed[1:60], - # delays - generation_time = generation_time_opts(generation_time), - delays = delay_opts( - incubation_period + reporting_delay - ), - # computation - stan = stan_opts( - cores = 4, samples = 1000, chains = 3, - control = list(adapt_delta = 0.99) - ) -) -``` +::::::::::::::::::::::::::::::::: challenge -## Severity parameters +### WHAT TYPE OF TRANSMISSION? -The three examples above represent transmission parameters. +Compare the serial interval and incubation period of Influenza and MERS: -One example of severity parameter is the duration of hospitalization (bed capacity needed) +- What type of transmission have Influenza? +- What type of transmission have MERS? +- Does these results correlates with the available evidence? -### explore +::::::::::::::::: hint -```{r} -epidist_db(disease = "all") %>% list_distributions() %>% - as_tibble() %>% - distinct(epi_distribution) -``` +Key functions: -## From our data! (callout) +- `epidist_db()` +- `epidist$summary_stats$` -- How to get serial interval from case data? -- how long should contacts be followed? -- what is the required duration of contact tracing? -- how long should cases be isolated to reduce transmission? +:::::::::::::::::::::: -An interesting example is the `method_assess` nested entry, which refers to the methods used to assess for the bias while estimating the serial interval distribution. +::::::::::::::::: solution ```{r} -covid_serialint$method_assess +# pre-symptomatic transmission +epidist_db(disease = "influenza",epi_dist = "incubation",single_epidist = T)$summary_stats$mean +epidist_db(disease = "influenza",epi_dist = "serial",single_epidist = T)$summary_stats$mean + +# symptomatic transmission +epidist_db(disease = "mers",epi_dist = "incubation",single_epidist = T)$summary_stats$median +epidist_db(disease = "mers",epi_dist = "serial",single_epidist = T)$summary_stats$mean ``` -Fit a parametric distribution to estimate the mean and sd +```{r,eval=FALSE} +# pre-symptomatic transmission +epidist_db(disease = "covid",epi_dist = "incubation",author = "Stephen",single_epidist = T)$summary_stats$mean +epidist_db(disease = "covid",epi_dist = "serial",author = "Nishiura",single_epidist = T)$summary_stats$mean -estimate serial interval from case and contact data pairs +# symptomatic transmission +epidist_db(disease = "ebola",epi_dist = "incubation",single_epidist = T)$summary_stats$mean +epidist_db(disease = "ebola",epi_dist = "serial",single_epidist = T)$summary_stats$mean +``` - +:::::::::::::::::::::::::: -ref: https://epiverse-trace.github.io/epimodelac/EnfermedadX.html +::::::::::::::::::::::::::::::::::::::::::: -Recall that "Methods for accurate estimation of the generation interval from contact tracing data involve adjusting for right truncation and accounting for population susceptibility at the times transmission pairs are observed" [(Gostic et al., 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409) ## G [Generation time]{#generationtime} : Time between the onset of infectiousness of an index case and its secondary case. This always needs to be positive. The generation time distribution is commonly estimated from data on the [serial interval](#serialinterval) distribution of an infection ([Cori et al. 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). -Different sampling approaches can generate biases given left and right censoring in the estimation of the serial interval that can propagate bias to the estimation of the [incubation period](#incubation) and generation time ([Chen et al., 2022](https://www.nature.com/articles/s41467-022-35496-8/figures/2)). @@ -101,7 +103,11 @@ This can be different to the [incubation period](#incubation) as shown in Figure [Offspring distribution]{#offspringdist} : Distribution of the number of secondary cases caused by a particular infected individual. ([Lloyd-Smith et al., 2005](https://www.nature.com/articles/nature04153), [Endo et al., 2020](https://wellcomeopenresearch.org/articles/5-67/v3)) - +## P + +[(Dynamical or Epidemic) Phase bias]{#phasebias} +: Accounts for population susceptibility at the times transmission pairs are observed. +It is a type of sampling bias. It affects backward-looking data and is related to the phase of the epidemic: during the exponential growth phase, cases that developed symptoms recently are over-represented in the observed data, while during the declining phase, these cases are underrepresented, leading to the estimation of shorter and longer delay intervals, respectively. ([Park et al., in progress](https://github.com/parksw3/epidist-paper)) @@ -125,7 +131,10 @@ The relationship between the serial interval and the [incubation period](#incuba : A model that includes some stochastic process resulting in variation in model simulations for the same initial conditions and parameter values. Examples include stochastic differential equations and branching process models. For more detail see [Allen (2017)](https://doi.org/10.1016/j.idm.2017.03.001). - +## T + +[(Right) Truncation]{#truncation} +: Type of sampling bias related to the data collection process. It arises because only cases that have been reported can be observed. Not accounting for right truncation during the growth phase of an epidemic can lead to underestimation of the mean delay ([Park et al., in progress](https://github.com/parksw3/epidist-paper)). From e19c4c5fe91123d620bc313ba7da8c382aec732b Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 23 Dec 2023 05:29:08 +0000 Subject: [PATCH 113/130] reduce to lower keys on subtitle --- episodes/read-delays.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 7e1401ee..94429760 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -1192,7 +1192,7 @@ Meta-analysis on the proportion of presymptomatic and asymptomatic transmission ::::::::::::::::::::::::::::::::: challenge -### WHAT TYPE OF TRANSMISSION? +### What type of transmission? Compare the serial interval and incubation period of Influenza and MERS: From 66f884783321dcb770ea8949d6ca90ce5a5fe9c2 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 23 Dec 2023 05:54:33 +0000 Subject: [PATCH 114/130] edit with grammarly --- episodes/read-delays.Rmd | 177 +++++++++++++++++++-------------------- 1 file changed, 88 insertions(+), 89 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 94429760..058a0947 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -10,7 +10,7 @@ editor_options: - How to get delay distributions from a systematic review? - How to connect reused delays with my existing analysis pipeline? -- When to reuse delays from a systematic review? +- When should delays be reused from a systematic review? :::::::::::::::::::::::::::::::::::::::::::::::: @@ -81,7 +81,7 @@ generation_time <- dist_spec( ) ``` -Let's explore how we can access to this and other time delays using `{epiparameter}`. We'll use the pipe `%>%` to connect some of their functions, so let's also call to the `{tidyverse}` package: +Let's explore how we can access this and other time delays using `{epiparameter}`. We'll use the pipe `%>%` to connect some of their functions, so let's also call to the `{tidyverse}` package: ```{r,warning=FALSE,message=FALSE} library(epiparameter) @@ -96,7 +96,7 @@ The generation time, jointly with the $R$, can inform about the speed of spread ![Video from the MRC Centre for Global Infectious Disease Analysis, Ep 76. Science In Context - Epi Parameter Review Group with Dr Anne Cori (27-07-2023) at ](fig/reproduction-generation-time.png) In calculating the effective reproduction number ($R_{t}$), the *generation time* distribution is often approximated by the [serial interval](../learners/reference.md#serialinterval) distribution. -This is because it is easier to observe and measure the onset of symptoms than the onset of infectiousness. +This frequent approximation is because it is easier to observe and measure the onset of symptoms than the onset of infectiousness. ![A schematic of the relationship of different time periods of transmission between an infector and an infectee in a transmission pair. Exposure window is defined as the time interval having viral exposure, and transmission window is defined as the time interval for onward transmission with respect to the infection time ([Chung Lau et al. 2021](https://academic.oup.com/jid/article/224/10/1664/6356465)).](fig/serial-interval-observed.jpeg) @@ -110,15 +110,15 @@ Additionally, even if the *generation time* and *serial interval* have the same When we calculate the *serial interval*, we see that not all case pairs have the same time length. We will observe this variability for any case pair and individual time period, including the [incubation period](../learners/reference.md#incubation) and [infectious period](../learners/reference.md#infectiousness). -![Serial intervals of possible case pairs in (a) COVID-19 and (b) MERSCoV. Pairs represent a presumed infector and their presumed infectee plotted by date of symptom onset ([Althobaity et al., 2022](https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig6)).](fig/serial-interval-pairs.jpg) +![Serial intervals of possible case pairs in (a) COVID-19 and (b) MERS-CoV. Pairs represent a presumed infector and their presumed infectee plotted by date of symptom onset ([Althobaity et al., 2022](https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig6)).](fig/serial-interval-pairs.jpg) -To summarize these data from individual and pair time periods, we can find the **statistical distributions** that best fits to the data ([McFarland et al., 2023](https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2023.28.27.2200806)). +To summarize these data from individual and pair time periods, we can find the **statistical distributions** that best fit the data ([McFarland et al., 2023](https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2023.28.27.2200806)). - + -![Fitted serial interval distribution for (a) COVID-19 and (b) MERS-CoV based on reported transmission pairs in Saudi Arabia. We fitted three commonly used distribution, lognormal, gamma, and Weibull distributions, respectively ([Althobaity et al., 2022](https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig5)).](fig/seria-interval-fitted-distributions.jpg) +![Fitted serial interval distribution for (a) COVID-19 and (b) MERS-CoV based on reported transmission pairs in Saudi Arabia. We fitted three commonly used distributions, Lognormal, Gamma, and Weibull distributions, respectively ([Althobaity et al., 2022](https://www.sciencedirect.com/science/article/pii/S2468042722000537#fig5)).](fig/seria-interval-fitted-distributions.jpg) -Statistical distributions are summarized in terms of their **summary statistics** like the *location* (mean and percentiles) and *spread* (variance or standard deviation) of the distribution, or with their **distribution parameters** that inform about the *form* (shape and rate/scale) of the distribution. Each of these estimated values can be reported with their **uncertainty** (95% confidence intervals). +Statistical distributions are summarized in terms of their **summary statistics** like the *location* (mean and percentiles) and *spread* (variance or standard deviation) of the distribution, or with their **distribution parameters** that inform about the *form* (shape and rate/scale) of the distribution. These estimated values can be reported with their **uncertainty** (95% confidence intervals). | Gamma | mean | shape | rate/scale | |:--------------|:--------------|:--------------|:--------------| @@ -135,7 +135,7 @@ Statistical distributions are summarized in terms of their **summary statistics* | MERS-CoV | 14.08(13.1–15.2) | 2.58(2.50–2.68) | 0.44(0.39–0.5) | | COVID-19 | 5.2(4.2–6.5) | 1.45(1.31–1.61) | 0.63(0.54–0.74) | -Table: Serial interval estimates using Gamma, Weibull and Log normal distributions. 95% confidence intervals for the shape and scale (logmean and sd for log normal) parameters are shown in brackets ([Althobaity et al., 2022](https://www.sciencedirect.com/science/article/pii/S2468042722000537#tbl3)). +Table: Serial interval estimates using Gamma, Weibull, and Log normal distributions. 95% confidence intervals for the shape and scale (logmean and sd for Log normal) parameters are shown in brackets ([Althobaity et al., 2022](https://www.sciencedirect.com/science/article/pii/S2468042722000537#tbl3)). ::::::::::::::::::::::::: @@ -143,7 +143,7 @@ Table: Serial interval estimates using Gamma, Weibull and Log normal distributio ### Serial interval -Assume that COVID-19 and SARS have similar reproduction number values and that the serial interval is a fair approximation of the generation time. +Assume that COVID-19 and SARS have similar reproduction number values and that the serial interval approximates the generation time. Given the Serial interval of both infections in the figure below: @@ -166,7 +166,7 @@ Which one would be harder to control? Why do you conclude that? -- COVID-19 has the lowest mean serial interval. The approximate mean value for the serial interval of COVID-19 is around four days, and SARS is about seven days. Thus, COVID-19 will likely have newer generations in less time than SARS, assuming similar reproduction numbers in both. +- COVID-19 has the lowest mean serial interval. The approximate mean value for the serial interval of COVID-19 is around four days, and SARS is about seven days. Thus, COVID-19 will likely have newer generations in less time than SARS, assuming similar reproduction numbers. :::::::::::::::::::::::::: @@ -174,15 +174,15 @@ Why do you conclude that? :::::::::::::::::::::: instructor -The objective of the assessment above is to assess on the interpretation of a larger or shorter generation time. +The objective of the assessment above is to assess the interpretation of a larger or shorter generation time. :::::::::::::::::::::: ## Extract epidemiological parameters -First, let's assume that the data set `example_confirmed` have COVID-19 observed cases. So, we need to find a reported generation time for COVID-19 or any other useful parameter for this aim. +First, let's assume that the data set `example_confirmed` has COVID-19 observed cases. So, we need to find a reported generation time for COVID-19 or any other useful parameter for this aim. -Let's start by looking at how many parameters do we have in the epidemiological distributions database (`epidist_db`) for the `disease` named `covid`-19: +Let's start by looking at how many parameters we have in the epidemiological distributions database (`epidist_db`) for the `disease` named `covid`-19: ```{r} epiparameter::epidist_db( @@ -190,9 +190,9 @@ epiparameter::epidist_db( ) ``` -From the `{epiparameter}` package, we can use the `epidist_db()` function to ask for any `disease` and also for an specific epidemiological distribution (`epi_dist`). +From the `{epiparameter}` package, we can use the `epidist_db()` function to ask for any `disease` and also for a specific epidemiological distribution (`epi_dist`). -Let's ask now how many parameters do we have in the epidemiological distributions database (`epidist_db`) with the generation time using the string `generation`: +Let's ask now how many parameters we have in the epidemiological distributions database (`epidist_db`) with the generation time using the string `generation`: ```{r} epiparameter::epidist_db( @@ -200,7 +200,7 @@ epiparameter::epidist_db( ) ``` -Currently, in the library of epidemiological parameters we have one `generation` time entry but for Influenza. Taking the considerations explained above, we can try to look at the `serial` intervals for `COVID`-19. +Currently, in the library of epidemiological parameters, we have one `generation` time entry for Influenza. Considering the abovementioned considerations, we can look at the `serial` intervals for `COVID`-19. ```{r} epiparameter::epidist_db( @@ -217,7 +217,7 @@ epiparameter::epidist_db( ::::::::::::::::::::::::: -We get more than one epidemiological delay. To summarize this view and get the column names from the underlying parameter dataset we can add the previous code the `epiparameter::list_distributions()` function using the pipe `%>%`: +We get more than one epidemiological delay. To summarize this view and get the column names from the underlying parameter dataset, we can add the `epiparameter::list_distributions()` function to the previous code using the pipe `%>%`: ```{r} epiparameter::epidist_db( @@ -258,16 +258,16 @@ epiparameter::epidist_db( Looking at the help documentation for `?epiparameter::epidist_db()`: - If multiple entries match the arguments supplied and `single_epidist = TRUE`, -- Then the `⁠`⁠ that is parameterised and has the largest sample size will be returned. -- If multiple entries are equal after this sorting the first entry will be returned. +- Then, the parameterised `⁠`⁠ with the largest sample size will be returned. +- If multiple entries are equal after this sorting, the first entry will be returned. What does a parametrised `` is? Look at `?is_parameterised`. ::::::::::::::::::::::::: -Now we have an epidemiological parameter we can reuse! We can use it to replace the numbers we plug-in to `EpiNow2::dist_spec()`. +Now, we have an epidemiological parameter we can reuse! We can replace the numbers we plug into `EpiNow2::dist_spec()`. -Let's do this by assigning this `` class object to the `covid_serialint` object. +Let's assign this `` class object to the `covid_serialint` object. ```{r} covid_serialint <- @@ -291,7 +291,7 @@ Take 5 minutes: - How many delay distributions are for the incubation period of Ebola? -- What type of distribution has the incubation period of Ebola with highest sample size? +- What type of distribution has the incubation period of Ebola with the highest sample size? ::::::::::::::::: hint @@ -355,7 +355,7 @@ Take 1 minute to: Share about: -- What elements you find useful for your analysis? +- What elements do you find useful for your analysis? - What other elements would you like to see in this object? How? ::::::::: hint @@ -380,7 +380,7 @@ covid_serialint$summary_stats$sd covid_serialint$metadata$sample_size ``` -An interesting element is the `method_assess` nested entry, which refers to the methods used by the study authors to assess for the bias while estimating the serial interval distribution. +An interesting element is the `method_assess` nested entry, which refers to the methods used by the study authors to assess for bias while estimating the serial interval distribution. ```{r} covid_serialint$method_assess @@ -418,7 +418,7 @@ distribution[[1]]$prob_dist ### Parameterised entries have an Inference method -As detailed in `?is_parameterised`, a parameterised distribution is the entry that has a probability distribution associated to it provided by an `inference_method` as shown in `metadata`: +As detailed in `?is_parameterised`, a parameterised distribution is the entry that has a probability distribution associated with it provided by an `inference_method` as shown in `metadata`: ```{r,eval=FALSE} distribution[[1]]$metadata$inference_method @@ -428,14 +428,14 @@ distribution[[4]]$metadata$inference_method ::::::::::::::::::::::::: -In the `epiparameter::list_distributions()` output, we can also find different types of probability distributions (e.g., log-normal, weibull, normal). +In the `epiparameter::list_distributions()` output, we can also find different types of probability distributions (e.g., log-normal, Weibull, normal). ```{r} distribution %>% list_distributions() ``` -In `{epiparameter}` you will mostly find **continuous** distributions like these. You can visualize any of them with the `plot()` function and access to: +In `{epiparameter}`, you will mostly find **continuous** distributions like these. You can visualize any of them with the `plot()` function and access to: - the *Probability Density Function (PDF)* and - the *Cumulative Distribution Function (CDF)*. @@ -444,7 +444,7 @@ In `{epiparameter}` you will mostly find **continuous** distributions like these plot(distribution[[2]]) ``` -With the `day_range` argument you can change the length or number of days in the `x` axis. Explore how does it looks like: +With the `day_range` argument, you can change the length or number of days in the `x` axis. Explore what it look like: ```{r,eval=FALSE} plot(distribution[[2]], day_range = 0:20) @@ -462,13 +462,13 @@ Follow these steps to reproduce the form of the `covid_serialint` distribution: - Go to the left panel, - Keep the *Category of distribution*: `Continuous Univariate`, - Select a new *Type of distribution*: `Log-Normal`, -- Move the **sliders**, i.e. the graphical control element that allows you to adjust a value by moving a handle along a horizontal track or bar, to the `covid_serialint` parameters. +- Move the **sliders**, i.e. the graphical control element that allows you to adjust a value by moving a handle along a horizontal track or bar to the `covid_serialint` parameters. -Replicate these with the `distribution` object and all its list elements: `2`, `3`, and `4`. Explore How does the shape of a distribution change when its parameters change. +Replicate these with the `distribution` object and all its list elements: `2`, `3`, and `4`. Explore how the shape of a distribution changes when its parameters change. Share about: -- What other features in the website you find useful? +- What other features of the website do you find helpful? ::::::::::::::::::::::::: @@ -476,24 +476,24 @@ Share about: ## Distribution functions -In R, all the statistical distributions has functions to access to the: +In R, all the statistical distributions have functions to access the: - Probability **Density** function (PDF), - Cumulative Distribution function (**CDF**), - **Quantile** function, and -- **Random** drawn values from the given distribution. +- **Random** values from the given distribution. :::::::::::: spoiler ### Functions for the Normal distribution -If you needed it, read in detail about the [R probability functions for the normal distribution](https://sakai.unc.edu/access/content/group/3d1eb92e-7848-4f55-90c3-7c72a54e7e43/public/docs/lectures/lecture13.htm#probfunc), each of its definitions and identify in which part of a distribution they are located! +If you need it, read in detail about the [R probability functions for the normal distribution](https://sakai.unc.edu/access/content/group/3d1eb92e-7848-4f55-90c3-7c72a54e7e43/public/docs/lectures/lecture13.htm#probfunc), each of its definitions and identify in which part of a distribution they are located! ![The four probability functions for the normal distribution ([Jack Weiss, 2012](https://sakai.unc.edu/access/content/group/3d1eb92e-7848-4f55-90c3-7c72a54e7e43/public/docs/lectures/lecture13.htm#probfunc))](fig/fig5a-normaldistribution.png) :::::::::::::::::::: -If you look at `?stats::Distributions`, each type of distribution has a unique set functions. However, `{epiparameter}` allows gives you one same set of four functions to access to each of the values above for any `` object you want! +If you look at `?stats::Distributions`, each type of distribution has a unique set of functions. However, `{epiparameter}` gives you the same four functions to access each of the values above for any `` object you want! ```{r,eval=FALSE} # plot this to have a visual reference @@ -553,9 +553,9 @@ cdf(covid_serialint,q = 6) Given the COVID-19 serial interval: -- A contact tracing method that consider contacts up to 2 days pre-onset will already capture around 11.1% of backwards cases. +- A contact tracing method considering contacts up to 2 days pre-onset will capture around 11.1% of backward cases. -- If this period was extended to 6 days pre-onset then this could include 76.2% of backward contacts. +- If this period is extended to 6 days pre-onset, this could include 76.2% of backward contacts. :::::::::::::::::::::::::: @@ -565,7 +565,7 @@ Given the COVID-19 serial interval: If we exchange the question between days and cumulative probability to: -- When considering secondary cases, after how many days following the symptom onset of primary cases can we expect 55% of symptom onset to occur? +- When considering secondary cases, how many days following the symptom onset of primary cases can we expect 55% of symptom onset to occur? ```{r} quantile(covid_serialint, p = 0.55) @@ -585,9 +585,9 @@ An interpretation could be: We are almost halfway! `EpiNow2::dist_spec()` still needs a maximum value (`max`). -One way to do this is getting the quantile value for the the 99.9th percentile or `0.999` cumulative probability of the distribution. For this we need to access to the set of distribution functions for our `` object. +One way to do this is to get the quantile value for the distribution's 99.9th percentile or `0.999` cumulative probability. For this, we need access to the set of distribution functions for our `` object. -We can use the set of distribution functions for a _continuous_ distribution (as above). However, this values will be _continuous_ numbers. To get discrete values from a continuous distribution we can **discretize** the continuous distribution stored in our `` object. +We can use the set of distribution functions for a _continuous_ distribution (as above). However, these values will be _continuous_ numbers. We can **discretize** the continuous distribution stored in our `` object to get discrete values from a continuous distribution. When we `epiparameter::discretise()` the continuous distribution we get a **discrete**(-ized) distribution: @@ -614,7 +614,7 @@ plot(covid_serialint) plot(covid_serialint_discrete) ``` -To finally get a `max` value let's access to the quantile value of the 99.9th percentile or `0.999` probability of the distribution with the `prob_dist$q` notation, similarly to how we access to the `summary_stats` values. +To finally get a `max` value, let's access the quantile value of the 99.9th percentile or `0.999` probability of the distribution with the `prob_dist$q` notation, similarly to how we access the `summary_stats` values. ```{r} covid_serialint_discrete_max <- @@ -625,7 +625,7 @@ covid_serialint_discrete_max <- ### Lenght of quarantine and Incubation period -The **incubation period** distribution is a useful delay to assess for the length of active monitoring or quarantine ([Lauer et al., 2020](https://www.acpjournals.org/doi/10.7326/M20-0504)). Similarly, delays from symptom onset to recovery (or death) will determine the required duration of health-care and case isolation ([Cori et al., 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). +The **incubation period** distribution is a useful delay to assess the length of active monitoring or quarantine ([Lauer et al., 2020](https://www.acpjournals.org/doi/10.7326/M20-0504)). Similarly, delays from symptom onset to recovery (or death) will determine the required duration of health-care and case isolation ([Cori et al., 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). Calculate: @@ -633,9 +633,9 @@ Calculate: ::::::::::::::::: hint -What is the delay distribution that measures the time between infection and symptoms onset? +What delay distribution measures the time between infection and the onset of symptoms? -Probability function for `` **discrete** distributions differ from the *continuous* ones! +The probability function for `` **discrete** distributions differ from the *continuous* ones! ```{r,eval=FALSE} # plot to have a visual reference @@ -647,7 +647,7 @@ covid_serialint_discrete$prob_dist$d(10) # cumulative probability at quantile value 10 (day) covid_serialint_discrete$prob_dist$cdf(10) -# in what quantile value (days) we have the 60% cumulative probability? +# In what quantile value (days) do we have the 60% cumulative probability? covid_serialint_discrete$prob_dist$q(0.6) # covid_serialint_discrete$prob_dist$qf(0.6) # as in continuous distribution @@ -662,7 +662,7 @@ tibble( quantile_values = seq(1L, to = x_limit, by = 1L) ) %>% mutate( - # calculate the values for each quantiles in the density function + # calculate the values for each quantile in the density function density_values = covid_serialint_discrete$prob_dist$d(quantile_values) ) %>% ggplot(aes( @@ -702,7 +702,7 @@ _Is this result expected in epidemiological terms?_ ### Ebola's severity parameter -A severity parameter like the duration of hospitalization could add into the information needed about the bed capacity in response to an outbreak ([Cori et al., 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). +A severity parameter like the duration of hospitalization could add to the information needed about the bed capacity in response to an outbreak ([Cori et al., 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). For Ebola: @@ -743,7 +743,7 @@ ebola_severity$summary_stats$mean_ci_limits ## Plug-in `{epiparameter}` to `{EpiNow2}` -Now we are ready to plug-in all to the `EpiNow2::dist_spec()` function! +Now we can plug everything into the `EpiNow2::dist_spec()` function! ```{r} serial_interval_covid <- @@ -761,11 +761,11 @@ serial_interval_covid ### Warning -Using the serial interval instead of the generation time is an alternative that can propagate bias in your estimates. Even more in diseases with reported pre-symptomatic transmission. +Using the serial interval instead of the generation time is an alternative that can propagate bias in your estimates, even more so in diseases with reported pre-symptomatic transmission. :::::::::::::::::: -Now, let's replace the `generation_time` input that we used for `EpiNow2::epinow()`. +Let's replace the `generation_time` input we used for `EpiNow2::epinow()`. ```{r,eval=FALSE} epinow_estimates <- epinow( @@ -790,7 +790,7 @@ base::plot(epinow_estimates) Download the [Ebola dataset](data/ebola_cases.csv): - Reuse one epidemiological parameter to estimate the effective reproduction number for the Ebola dataset. -- Why you choose that parameter? +- Why did you choose that parameter? ```{r,eval=FALSE} ebola_confirmed <- read_csv("data/raw-data/ebola_cases.csv") @@ -798,12 +798,12 @@ ebola_confirmed <- read_csv("data/raw-data/ebola_cases.csv") ::::::::::::::::: hint -To calculate the Rt we need: +To calculate the $R_t$, we need: -- data set with confirmed cases per day, and +- data set with confirmed cases per day and - one key delay distribution -Key functions we applied in this episodes are: +Key functions we applied in this episode are: - `epidist_db()` - `list_distributions()` @@ -815,7 +815,7 @@ Key functions we applied in this episodes are: ::::::::::::::::: solution ```{r,eval=FALSE} -# read data from tutorial repository R project +# read data from the tutorial repository R project ebola_confirmed <- read_csv("episodes/data/ebola_cases.csv") ``` @@ -844,7 +844,7 @@ serial_interval_ebola <- distribution = "gamma" ) -# name of type of distribution +# name of the type of distribution # only for the discretised distribution ebola_serial_discrete$prob_dist$name @@ -887,8 +887,8 @@ For `{EpiNow2}`, we can specify these complementary delay distributions in the ` Use `{epiparameter}` to: -- Find and incubation period for COVID-19. -- Add it our last `epinow()` code chunk using the `delays` argument and the `delay_opts()` helper function. +- Find an incubation period for COVID-19. +- Add our last `epinow()` code chunk using the `delays` argument and the `delay_opts()` helper function. ::::::::::::::::: hint @@ -953,12 +953,12 @@ base::plot(epinow_estimates) :::::::::::::: solution -### How much it changed? +### How much has it changed? After adding the incubation period, discuss: -- Does the retrospective trend of forecast changed? -- Does the uncertainty changed? +- Does the retrospective trend of forecast change? +- Has the uncertainty changed? - How would you explain or interpret any of these changes? :::::::::::::::::::::::::::: @@ -968,13 +968,13 @@ After adding the incubation period, discuss: ::::::::::::::::::::::::::::::::: challenge -### Ebola's effective reproduction number adjusted by reporting delays +### Ebola's effective reproduction number was adjusted by reporting delays Using the same [Ebola dataset](data/ebola_cases.csv): - Reuse one additional epidemiological parameter for the `delays` argument in `EpiNow2::epinow()`. - Estimate the effective reproduction number using `EpiNow2::epinow()`. -- Why you choose that parameter? +- Why did you choose that parameter? ```{r,eval=FALSE} ebola_confirmed <- read_csv("data/raw-data/ebola_cases.csv") @@ -982,14 +982,14 @@ ebola_confirmed <- read_csv("data/raw-data/ebola_cases.csv") ::::::::::::::::: hint -There are two complementary delay distributions that we can use to estimate the $R_t$ at time $t$. +We can use two complementary delay distributions to estimate the $R_t$ at time $t$. :::::::::::::::::::::: ::::::::::::::::: solution ```{r,eval=FALSE} -# read data from tutorial repository R project +# read data from the tutorial repository R project ebola_confirmed <- read_csv("episodes/data/ebola_cases.csv") ``` @@ -1018,7 +1018,7 @@ serial_interval_ebola <- distribution = "gamma" ) -# subset one distribution for delay of incubation period +# subset one distribution for delay of the incubation period ebola_incubation <- epidist_db( disease = "ebola", epi_dist = "incubation", @@ -1076,7 +1076,7 @@ How to get the mean and standard deviation from a generation time with median an ```{r,eval=FALSE} -# what parameters are are available for influenza? +# What parameters are available for Influenza? epidist_db(disease = "influenza") %>% list_distributions() %>% as_tibble() %>% @@ -1087,7 +1087,7 @@ influenza_generation <- epidist_db(disease = "influenza",epi_dist = "generation" influenza_generation_discrete <- discretise(influenza_generation) # problem -# the summary statistics fo not have mean and sd +# the summary statistics do not have mean and sd influenza_generation$summary_stats influenza_generation$summary_stats$median influenza_generation$summary_stats$quantiles @@ -1144,16 +1144,15 @@ plot(epinow_estimates) ## When to reuse? When to estimate? -In the early stage of an outbreak we can rely on reusing parameters, for known and unknown pathogens, even for the [Disease X](https://en.wikipedia.org/wiki/Disease_X), a pathogen currently unknown to cause human disease and potentially cause a serious international epidemic ([WHO, 2018](https://www.who.int/activities/prioritizing-diseases-for-research-and-development-in-emergency-contexts)). +In the early stage of an outbreak, we can rely on reusing parameters for known and unknown pathogens, even for the [Disease X](https://en.wikipedia.org/wiki/Disease_X), a pathogen currently unknown to cause human disease and potentially cause a serious international epidemic ([WHO, 2018](https://www.who.int/activities/prioritizing-diseases-for-research-and-development-in-emergency-contexts)). -But when data from **lines list paired with contact tracing** is available, we are in the possibility to estimate the key delays distributions that best fit our data. This will help us to inform, compare and update any previous estimate about questions like: +But when data from **lines list paired with contact tracing** is available, we can estimate the key delay distributions that best fit our data. These will help us to inform, compare and update any previous estimate about questions like: -- How to get serial interval from case data? -- how long should contacts be followed? -- what is the required duration of contact tracing? -- how long should cases be isolated to reduce transmission? +- How long should contacts be followed? +- What is the required duration of contact tracing? +- How long should cases be isolated to reduce transmission? -However, the methods to accurately estimate delays like the generation interval from contact tracing data involve adjusting for biases like like **[censoring](../learners/reference.md#censoring)**, **[right truncation](../learners/reference.md#truncation)** and **[epidemic phase bias](../learners/reference.md#phasebias)**. [(Gostic et al., 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409) +However, the methods to accurately estimate delays like the generation interval from contact tracing data involve adjusting for biases like **[censoring](../learners/reference.md#censoring)**, **[right truncation](../learners/reference.md#truncation)** and **[epidemic phase bias](../learners/reference.md#phasebias)**. [(Gostic et al., 2020)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409) We can identify what entries in the `{epiparameter}` library assessed for these biases in their methodology with the `method_assess` nested entry: @@ -1163,11 +1162,11 @@ covid_serialint$method_assess ::::::::::::::::: testimonial -### How to estimating delay distributions for Disease X? +### How to estimate delay distributions for Disease X? -Refer to this excellent tutorial on how to estimate the serial interval and incubation period of Disease X accounting for *censoring* using Bayesian inference with packages like `{rstan}` and `{coarseDataTools}`. +Refer to this excellent tutorial on estimating the serial interval and incubation period of Disease X accounting for *censoring* using Bayesian inference with packages like `{rstan}` and `{coarseDataTools}`. -- Tutuorial in English: +- Tutorial in English: - Tutorial en Español: ::::::::::::::::::::::::::::: @@ -1176,17 +1175,17 @@ Refer to this excellent tutorial on how to estimate the serial interval and incu ### How to assess for pre-symptomatic infection? -The lengths of the Serial interval and Incubation period determine the type of transmission of a disease. +The lengths of the Serial interval and Incubation period determine the type of disease transmission. ![The relationship between the incubation period and serial interval. From [Nishiura 2020](https://www.ijidonline.com/article/S1201-9712(20)30119-3/fulltext)](fig/incubation-period-serial-interval.jpg) -Estimating the proportion of presymptomatic infections, or the extent to which infectiousness precedes symptom onset will determine the effectiveness of contact tracing and the feasibility of controlling an outbreak ([Fraser et al., 2004](https://www.pnas.org/doi/full/10.1073/pnas.0307506101) and [Hellewell et al., 2020](https://www.thelancet.com/article/S2214-109X(20)30074-7/fulltext)). +Estimating the proportion of pre-symptomatic infections, or the extent to which infectiousness precedes symptom onset will determine the effectiveness of contact tracing and the feasibility of controlling an outbreak ([Fraser et al., 2004](https://www.pnas.org/doi/full/10.1073/pnas.0307506101) and [Hellewell et al., 2020](https://www.thelancet.com/article/S2214-109X(20)30074-7/fulltext)). - + -![Parameter estimates. Plausible ranges for the key parameters R0 and θ (read main text for sources) for four viral infections of public concern are shown as shaded regions. The size of the shaded area reflects the uncertainties in the parameter estimates. [Fraser et al., 2004](https://www.pnas.org/doi/10.1073/pnas.0307506101)](fig/reproduction-number-pre-symptomatic.png) +![Parameter estimates. Plausible ranges for the key parameters R0 and θ (read the main text for sources) for four viral infections of public concern are shown as shaded regions. The size of the shaded area reflects the uncertainties in the parameter estimates. [Fraser et al., 2004](https://www.pnas.org/doi/10.1073/pnas.0307506101)](fig/reproduction-number-pre-symptomatic.png) -Meta-analysis on the proportion of presymptomatic and asymptomatic transmission in SARS-CoV-2 found limitations of the evidence given high heterogeneity and high risk of selection and information bias between studies ([Buitrago-Garcia et al., 2022](https://journals.plos.org/plosmedicine/article/figure?id=10.1371/journal.pmed.1003987.g003)). This is a call to action to improve the Outbreak Analytic pipelines to use and reuse in the early phase of an outbreak. +Meta-analysis on the proportion of pre-symptomatic and asymptomatic transmission in SARS-CoV-2 found limitations of the evidence given high heterogeneity and high risk of selection and information bias between studies ([Buitrago-Garcia et al., 2022](https://journals.plos.org/plosmedicine/article/figure?id=10.1371/journal.pmed.1003987.g003)). This is a call to action to improve the Outbreak Analytic pipelines to use and reuse in the early phase of an outbreak. ::::::::::::::::::::::::::::::::::: @@ -1196,9 +1195,9 @@ Meta-analysis on the proportion of presymptomatic and asymptomatic transmission Compare the serial interval and incubation period of Influenza and MERS: -- What type of transmission have Influenza? -- What type of transmission have MERS? -- Does these results correlates with the available evidence? +- What type of transmission has Influenza? +- What type of transmission has MERS? +- Do these results correlate with the available evidence? ::::::::::::::::: hint @@ -1244,9 +1243,9 @@ update it from last epiparameter test ::::::::::::::::::::::::::::::::::::: keypoints -- Use `{epiparameter}` to access to systematic review catalog of epidemiological delay distributions. +- Use `{epiparameter}` to access the systematic review catalogue of epidemiological delay distributions. - Use `epidist_db()` to select single delay distributions. -- Use `list_distributions()` for a overview of multiple delay distributions. +- Use `list_distributions()` for an overview of multiple delay distributions. - Use `discretise()` to convert continuous to discrete delay distributions. - Use `{epiparameter}` probability functions for any delay distributions. From 915c6cea1b1ed90375f07b26d221f2363e1a3036 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 23 Dec 2023 06:33:53 +0000 Subject: [PATCH 115/130] edits after visual inspection of render website --- episodes/read-delays.Rmd | 135 +++++++++++++++++++++------------------ 1 file changed, 72 insertions(+), 63 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 058a0947..d845d071 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -296,6 +296,7 @@ Take 5 minutes: ::::::::::::::::: hint The `{epiparameter}` combo of `epidist_db()` plus `list_distributions()` list all the entries by: + - disease, - epidemiological distribution, - the type of the probability distribution, @@ -306,7 +307,7 @@ The `{epiparameter}` combo of `epidist_db()` plus `list_distributions()` list al ::::::::::::::::: solution -```{r} +```{r,eval=FALSE} # 16 delays distributions epiparameter::epidist_db( disease = "ebola" @@ -372,7 +373,7 @@ Use the `str()` to display the structure of the `` R object. :::::::::: solution -```{r} +```{r,eval=FALSE} # get the sd covid_serialint$summary_stats$sd @@ -380,6 +381,12 @@ covid_serialint$summary_stats$sd covid_serialint$metadata$sample_size ``` +:::::::::::::::::::: + +:::::::::::::::::::::::::::::::: + +:::::::::::::::::::: intructor + An interesting element is the `method_assess` nested entry, which refers to the methods used by the study authors to assess for bias while estimating the serial interval distribution. ```{r} @@ -388,9 +395,51 @@ covid_serialint$method_assess We will explore these concepts at the end! -:::::::::::::::::::: +:::::::::::::::::::::::::::::: + + +::::::::::::::::::::::::::::::::: challenge + +### Ebola's severity parameter + +A severity parameter like the duration of hospitalization could add to the information needed about the bed capacity in response to an outbreak ([Cori et al., 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). + +For Ebola: + +- what is a reported point estimate and uncertainty of the mean duration of health-care and case isolation? + +::::::::::::::::: hint + +An informative delay measures the time from symptom onset to recovery or death. + +:::::::::::::::::::::: + +::::::::::::::::: solution + +```{r,eval=FALSE} +# one way to get the list of all the available parameters +epidist_db(disease = "all") %>% + list_distributions() %>% + as_tibble() %>% + distinct(epi_distribution) + +ebola_severity <- epidist_db( + disease = "ebola", + epi_dist = "onset to discharge" +) + +# point estimate +ebola_severity$summary_stats$mean +# 95% confidence intervals +ebola_severity$summary_stats$mean_ci +# limits of the confidence intervals +ebola_severity$summary_stats$mean_ci_limits +``` + +:::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::::::::: -:::::::::::::::::::::::::::::::: ## Continuous distributions @@ -428,7 +477,7 @@ distribution[[4]]$metadata$inference_method ::::::::::::::::::::::::: -In the `epiparameter::list_distributions()` output, we can also find different types of probability distributions (e.g., log-normal, Weibull, normal). +In the `epiparameter::list_distributions()` output, we can also find different types of probability distributions (e.g., Log-normal, Weibull, Normal). ```{r} distribution %>% @@ -458,11 +507,11 @@ Explore this shinyapp called **The Distribution Zoo**! Follow these steps to reproduce the form of the `covid_serialint` distribution: -- Access to shinyapp website, -- Go to the left panel, -- Keep the *Category of distribution*: `Continuous Univariate`, -- Select a new *Type of distribution*: `Log-Normal`, -- Move the **sliders**, i.e. the graphical control element that allows you to adjust a value by moving a handle along a horizontal track or bar to the `covid_serialint` parameters. +1. Access to shinyapp website, +2. Go to the left panel, +3. Keep the *Category of distribution*: `Continuous Univariate`, +4. Select a new *Type of distribution*: `Log-Normal`, +5. Move the **sliders**, i.e. the graphical control element that allows you to adjust a value by moving a handle along a horizontal track or bar to the `covid_serialint` parameters. Replicate these with the `distribution` object and all its list elements: `2`, `3`, and `4`. Explore how the shape of a distribution changes when its parameters change. @@ -545,8 +594,11 @@ In Figure 5 from the [R probability functions for the normal distribution](https ::::::::::::::::: solution -```{r} +```{r,eval=FALSE} plot(covid_serialint) +``` + +```{r,eval=FALSE} cdf(covid_serialint,q = 2) cdf(covid_serialint,q = 6) ``` @@ -567,7 +619,7 @@ If we exchange the question between days and cumulative probability to: - When considering secondary cases, how many days following the symptom onset of primary cases can we expect 55% of symptom onset to occur? -```{r} +```{r,eval=FALSE} quantile(covid_serialint, p = 0.55) ``` @@ -583,7 +635,7 @@ An interpretation could be: ## Discretize a continuous distribution -We are almost halfway! `EpiNow2::dist_spec()` still needs a maximum value (`max`). +We are getting closer to the end! `EpiNow2::dist_spec()` still needs a maximum value (`max`). One way to do this is to get the quantile value for the distribution's 99.9th percentile or `0.999` cumulative probability. For this, we need access to the set of distribution functions for our `` object. @@ -676,7 +728,7 @@ tibble( ::::::::::::::::: solution -```{r} +```{r,eval=FALSE} covid_incubation <- epiparameter::epidist_db( disease = "covid", @@ -698,49 +750,6 @@ _Is this result expected in epidemiological terms?_ ::::::::::::::::::::::::::::::::::::::::::: -::::::::::::::::::::::::::::::::: challenge - -### Ebola's severity parameter - -A severity parameter like the duration of hospitalization could add to the information needed about the bed capacity in response to an outbreak ([Cori et al., 2017](https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0371)). - -For Ebola: - -- what is a reported point estimate and uncertainty of the mean duration of health-care and case isolation? - -::::::::::::::::: hint - -An informative delay measures the time from symptom onset to recovery or death. - -:::::::::::::::::::::: - -::::::::::::::::: solution - -```{r} -# one way to get the list of all the available parameters -epidist_db(disease = "all") %>% - list_distributions() %>% - as_tibble() %>% - distinct(epi_distribution) - -ebola_severity <- epidist_db( - disease = "ebola", - epi_dist = "onset to discharge" -) - -# point estimate -ebola_severity$summary_stats$mean -# 95% confidence intervals -ebola_severity$summary_stats$mean_ci -# limits of the confidence intervals -ebola_severity$summary_stats$mean_ci_limits -``` - -:::::::::::::::::::::::::: - -::::::::::::::::::::::::::::::::::::::::::: - - ## Plug-in `{epiparameter}` to `{EpiNow2}` Now we can plug everything into the `EpiNow2::dist_spec()` function! @@ -814,7 +823,7 @@ Key functions we applied in this episode are: ::::::::::::::::: solution -```{r,eval=FALSE} +```{r,eval=FALSE,echo=FALSE} # read data from the tutorial repository R project ebola_confirmed <- read_csv("episodes/data/ebola_cases.csv") ``` @@ -875,9 +884,9 @@ Estimating $R_t$ requires data on the daily number of new infections. Due to lag ![Timeline for chain of disease reporting, the Netherlands. Lab, laboratory; PHA, public health authority. From [Marinović et al., 2015](https://wwwnc.cdc.gov/eid/article/21/2/13-0504_article)](fig/disease-reporting.jpg) -The delay distribution could be inferred jointly with the underlying times of infection or estimated as the sum of the [incubation period](../learners/reference.md#incubation) distribution and the distribution of delays from symptom onset to observation from line list data ([reporting delay](../learners/reference.md#reportingdelay)). +The **delay distribution** could be inferred jointly with the underlying times of infection or estimated as the sum of the **[incubation period](../learners/reference.md#incubation)** distribution and the distribution of delays from symptom onset to observation from line list data **([reporting delay](../learners/reference.md#reportingdelay))**. -For `{EpiNow2}`, we can specify these complementary delay distributions in the `delays` argument. +For `{EpiNow2}`, we can specify these two complementary delay distributions in the `delays` argument. ![Rt is a measure of transmission at time t. Observations after time t must be adjusted. ICU, intensive care unit. From [Gostic et al., 2020](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409#sec007)](fig/rt-adjusting-delays.png) @@ -988,7 +997,7 @@ We can use two complementary delay distributions to estimate the $R_t$ at time $ ::::::::::::::::: solution -```{r,eval=FALSE} +```{r,eval=FALSE,echo=FALSE} # read data from the tutorial repository R project ebola_confirmed <- read_csv("episodes/data/ebola_cases.csv") ``` @@ -1144,7 +1153,7 @@ plot(epinow_estimates) ## When to reuse? When to estimate? -In the early stage of an outbreak, we can rely on reusing parameters for known and unknown pathogens, even for the [Disease X](https://en.wikipedia.org/wiki/Disease_X), a pathogen currently unknown to cause human disease and potentially cause a serious international epidemic ([WHO, 2018](https://www.who.int/activities/prioritizing-diseases-for-research-and-development-in-emergency-contexts)). +In the early stage of an outbreak, we can rely on reusing parameters for known pathogens to unknown ones, like for the [Disease X](https://en.wikipedia.org/wiki/Disease_X), a pathogen currently unknown to cause human disease and potentially cause a serious international epidemic ([WHO, 2018](https://www.who.int/activities/prioritizing-diseases-for-research-and-development-in-emergency-contexts)). But when data from **lines list paired with contact tracing** is available, we can estimate the key delay distributions that best fit our data. These will help us to inform, compare and update any previous estimate about questions like: @@ -1210,7 +1219,7 @@ Key functions: ::::::::::::::::: solution -```{r} +```{r,eval=FALSE} # pre-symptomatic transmission epidist_db(disease = "influenza",epi_dist = "incubation",single_epidist = T)$summary_stats$mean epidist_db(disease = "influenza",epi_dist = "serial",single_epidist = T)$summary_stats$mean From 385d4bfc60f7c2be624990480b7514bddbc1f32f Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 23 Dec 2023 06:48:30 +0000 Subject: [PATCH 116/130] relocate one challenge, remove eval in solutions --- episodes/read-delays.Rmd | 74 ++++++++++++++++++++++++++++++++-------- 1 file changed, 59 insertions(+), 15 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index d845d071..ff21833e 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -227,6 +227,52 @@ epiparameter::epidist_db( epiparameter::list_distributions() ``` +::::::::::::::::::::::::::::::::: challenge + +### Ebola's incubation periods + +Take 5 minutes: + +- How many delay distributions are for the Ebola disease? + +- How many delay distributions are for the incubation period of Ebola? + +- Explore the library and find the disease of interest! + +::::::::::::::::: hint + +The `{epiparameter}` combo of `epidist_db()` plus `list_distributions()` list all the entries by: + +- disease, +- epidemiological distribution, +- the type of the probability distribution, +- author of the study, and +- year of study. + +:::::::::::::::::::::: + +::::::::::::::::: solution + +```{r,eval=FALSE} +# 16 delays distributions +epiparameter::epidist_db( + disease = "ebola" +) + +# 5 delay distributions are for the incubation period +epiparameter::epidist_db( + disease = "ebola", + epi_dist = "incubation" +) +``` + +Now, from the output of `epiparameter::epidist_db()`, What is an [offspring distribution](../learners/reference.md#offspringdist)? + +:::::::::::::::::::::::::: + +::::::::::::::::::::::::::::::::::::::::::: + + ## Select a single distribution The `epiparameter::epidist_db()` function works as a filtering or subset function. Let's use the `author` argument to filter `Hiroshi Nishiura` parameters: @@ -285,13 +331,10 @@ covid_serialint ### Ebola's incubation period -Take 5 minutes: - -- How many delay distributions are for the Ebola disease? - -- How many delay distributions are for the incubation period of Ebola? +Take 2 minutes: - What type of distribution has the incubation period of Ebola with the highest sample size? +- How would you access to the sample size of the other studies in the `` class object? ::::::::::::::::: hint @@ -303,22 +346,21 @@ The `{epiparameter}` combo of `epidist_db()` plus `list_distributions()` list al - author of the study, and - year of study. -:::::::::::::::::::::: - -::::::::::::::::: solution +This is a `` class object: -```{r,eval=FALSE} -# 16 delays distributions -epiparameter::epidist_db( - disease = "ebola" -) - -# 5 delay distributions are for the incubation period +```{r} epiparameter::epidist_db( disease = "ebola", epi_dist = "incubation" ) +``` + + +:::::::::::::::::::::: +::::::::::::::::: solution + +```{r,eval=FALSE} # the distribution with the highest sample size has a gamma distribution epiparameter::epidist_db( disease = "ebola", @@ -1210,6 +1252,8 @@ Compare the serial interval and incubation period of Influenza and MERS: ::::::::::::::::: hint +For types of transmission, we refer to infections with symptomatic or pre-symptomatic transmission. + Key functions: - `epidist_db()` From 7fae3383a61060766b394716016aa3c996742088 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 23 Dec 2023 06:50:10 +0000 Subject: [PATCH 117/130] add renv file for early task 1 episodes --- renv/profiles/lesson-requirements/renv.lock | 71 ++++++++++++++++++--- 1 file changed, 63 insertions(+), 8 deletions(-) diff --git a/renv/profiles/lesson-requirements/renv.lock b/renv/profiles/lesson-requirements/renv.lock index b7a0a655..84dd13c7 100644 --- a/renv/profiles/lesson-requirements/renv.lock +++ b/renv/profiles/lesson-requirements/renv.lock @@ -136,7 +136,7 @@ }, "QuickJSR": { "Package": "QuickJSR", - "Version": "1.0.7", + "Version": "1.0.9", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -144,7 +144,7 @@ "Rcpp", "jsonlite" ], - "Hash": "1160fb7afae2eebb5f24939590956cdf" + "Hash": "1965c66cbe09d7878d3a8fa6ecbd8e71" }, "R.methodsS3": { "Package": "R.methodsS3", @@ -172,7 +172,7 @@ }, "R.utils": { "Package": "R.utils", - "Version": "2.12.2", + "Version": "2.12.3", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -183,7 +183,7 @@ "tools", "utils" ], - "Hash": "325f01db13da12c04d8f6e7be36ff514" + "Hash": "3dc2829b790254bfba21e60965787651" }, "R6": { "Package": "R6", @@ -1273,6 +1273,16 @@ ], "Hash": "eef74fe28b747e52288ea9e1d3600034" }, + "inline": { + "Package": "inline", + "Version": "0.3.19", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "methods" + ], + "Hash": "1deaf1de3eac7e1d3377954b3a283652" + }, "isoband": { "Package": "isoband", "Version": "0.2.7", @@ -1382,6 +1392,17 @@ ], "Hash": "b64ec208ac5bc1852b285f665d6368b3" }, + "lambda.r": { + "Package": "lambda.r", + "Version": "1.2.4", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R", + "formatR" + ], + "Hash": "b1e925c4b9ffeb901bacf812cbe9a6ad" + }, "lattice": { "Package": "lattice", "Version": "0.22-5", @@ -1447,6 +1468,30 @@ ], "Hash": "b8552d117e1b808b09a832f589b79035" }, + "listenv": { + "Package": "listenv", + "Version": "0.9.0", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R" + ], + "Hash": "4fbd3679ec8ee169ba28d4b1ea7d0e8f" + }, + "loo": { + "Package": "loo", + "Version": "2.6.0", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R", + "checkmate", + "matrixStats", + "parallel", + "stats" + ], + "Hash": "e5c8f41731502a0e98f353da23f7ca30" + }, "lubridate": { "Package": "lubridate", "Version": "1.9.3", @@ -1521,13 +1566,13 @@ }, "matrixStats": { "Package": "matrixStats", - "Version": "1.1.0", + "Version": "1.2.0", "Source": "Repository", "Repository": "CRAN", "Requirements": [ "R" ], - "Hash": "68d317093459a3f0852b1dd52d9e1ea6" + "Hash": "33a3ca9e732b57244d14f5d732ffc9eb" }, "memoise": { "Package": "memoise", @@ -1952,13 +1997,13 @@ }, "rprojroot": { "Package": "rprojroot", - "Version": "2.0.3", + "Version": "2.0.4", "Source": "Repository", "Repository": "CRAN", "Requirements": [ "R" ], - "Hash": "1de7ab598047a87bba48434ba35d497d" + "Hash": "4c8415e0ec1e29f3f4f6fc108bef0144" }, "rstan": { "Package": "rstan", @@ -2388,6 +2433,16 @@ ], "Hash": "5ac22900ae0f386e54f1c307eca7d843" }, + "truncnorm": { + "Package": "truncnorm", + "Version": "1.0-9", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R" + ], + "Hash": "ef5b32c5194351ff409dfb37ca9468f1" + }, "tzdb": { "Package": "tzdb", "Version": "0.4.0", From b949724973c4255140c4c6908c7949e03ba11eef Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Sat, 23 Dec 2023 07:04:09 +0000 Subject: [PATCH 118/130] add eval false to hints, edit one question and sol --- episodes/read-delays.Rmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index ff21833e..381998ba 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -237,7 +237,7 @@ Take 5 minutes: - How many delay distributions are for the incubation period of Ebola? -- Explore the library and find the disease of interest! +- Explore the library and find the disease with the delay distribution of your interest! Do you recognize the paper? ::::::::::::::::: hint @@ -348,7 +348,7 @@ The `{epiparameter}` combo of `epidist_db()` plus `list_distributions()` list al This is a `` class object: -```{r} +```{r,eval=FALSE} epiparameter::epidist_db( disease = "ebola", epi_dist = "incubation" @@ -369,7 +369,7 @@ epiparameter::epidist_db( ) ``` -Now, from the output of `epiparameter::epidist_db()`, What is an [offspring distribution](../learners/reference.md#offspringdist)? +To access the `sample_size`, review an [issue reported in the GitHub repository](https://github.com/epiverse-trace/epiparameter/issues/227) of the `{epiparameter}` package. :::::::::::::::::::::::::: From 306b5000182d892ffd82a900bcd53a23041fa2ac Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 19 Jan 2024 18:46:32 +0000 Subject: [PATCH 119/130] fix typo to use incubation instead of serial reported by https://github.com/epiverse-trace/howto/issues/34#issuecomment-1898323431 --- episodes/read-delays.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 381998ba..f8cfc144 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -979,7 +979,7 @@ covid_incubation_discrete <- epiparameter::discretise(covid_incubation) incubation_time_covid <- dist_spec( mean = covid_incubation$summary_stats$mean, sd = covid_incubation$summary_stats$sd, - max = covid_serialint_discrete$prob_dist$q(p = 0.999), + max = covid_incubation_discrete$prob_dist$q(p = 0.999), distribution = "lognormal" ) From 5a6aba86cdfaa9274ffec16674a8ee078eb50156 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Tue, 23 Jan 2024 11:30:05 +0000 Subject: [PATCH 120/130] remove redundant "delays" separator --- episodes/read-delays.Rmd | 1 - 1 file changed, 1 deletion(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index f8cfc144..1b7e4d26 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -988,7 +988,6 @@ epinow_estimates <- epinow( reported_cases = example_confirmed[1:60], # delays generation_time = generation_time_opts(serial_interval_covid), - # delays delays = delay_opts(incubation_time_covid), # computation stan = stan_opts( From 89b8666bbd11ac1146606d51a206bb5465c7d938 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Wed, 24 Jan 2024 18:55:36 +0000 Subject: [PATCH 121/130] update renv after resolve conflicts --- renv/profiles/lesson-requirements/renv.lock | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/renv/profiles/lesson-requirements/renv.lock b/renv/profiles/lesson-requirements/renv.lock index 84dd13c7..a4b2e3cb 100644 --- a/renv/profiles/lesson-requirements/renv.lock +++ b/renv/profiles/lesson-requirements/renv.lock @@ -1,6 +1,6 @@ { "R": { - "Version": "4.3.2", + "Version": "4.2.2", "Repositories": [ { "Name": "carpentries", @@ -1866,7 +1866,7 @@ "Package": "ragg", "Version": "1.2.7", "Source": "Repository", - "Repository": "RSPM", + "Repository": "https://carpentries.r-universe.dev", "Requirements": [ "systemfonts", "textshaping" @@ -2221,7 +2221,7 @@ "Package": "stringi", "Version": "1.8.3", "Source": "Repository", - "Repository": "RSPM", + "Repository": "https://carpentries.r-universe.dev", "Requirements": [ "R", "stats", @@ -2661,7 +2661,7 @@ "Package": "xml2", "Version": "1.3.6", "Source": "Repository", - "Repository": "RSPM", + "Repository": "https://carpentries.r-universe.dev", "Requirements": [ "R", "cli", From 53e18ea57ca308b9ce9760d33efb9cbe84ce8ba1 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Wed, 24 Jan 2024 19:21:14 +0000 Subject: [PATCH 122/130] add install.R file to solve RcppParallel gh action Following solution for similar issue in https://github.com/epiverse-trace/epimodelac/pull/11#issuecomment-1837203396 --- episodes/install.R | 1 + renv/profiles/lesson-requirements/renv.lock | 150 +++++++++++--------- 2 files changed, 86 insertions(+), 65 deletions(-) create mode 100644 episodes/install.R diff --git a/episodes/install.R b/episodes/install.R new file mode 100644 index 00000000..3af55f94 --- /dev/null +++ b/episodes/install.R @@ -0,0 +1 @@ +install.packages("RcppParallel") diff --git a/renv/profiles/lesson-requirements/renv.lock b/renv/profiles/lesson-requirements/renv.lock index a4b2e3cb..be78e40d 100644 --- a/renv/profiles/lesson-requirements/renv.lock +++ b/renv/profiles/lesson-requirements/renv.lock @@ -23,21 +23,21 @@ "Packages": { "BH": { "Package": "BH", - "Version": "1.81.0-1", + "Version": "1.84.0-0", "Source": "Repository", "Repository": "CRAN", - "Hash": "68122010f01c4dcfbe58ce7112f2433d" + "Hash": "a8235afbcd6316e6e91433ea47661013" }, "DBI": { "Package": "DBI", - "Version": "1.2.0", + "Version": "1.2.1", "Source": "Repository", "Repository": "CRAN", "Requirements": [ "R", "methods" ], - "Hash": "3e0051431dff9acfe66c23765e55c556" + "Hash": "9b4993e98e0e19da84c168460c032fef" }, "DiagrammeR": { "Package": "DiagrammeR", @@ -104,7 +104,7 @@ }, "MASS": { "Package": "MASS", - "Version": "7.3-60", + "Version": "7.3-60.0.1", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -115,11 +115,11 @@ "stats", "utils" ], - "Hash": "a56a6365b3fa73293ea8d084be0d9bb0" + "Hash": "b765b28387acc8ec9e9c1530713cb19c" }, "Matrix": { "Package": "Matrix", - "Version": "1.6-4", + "Version": "1.6-5", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -132,11 +132,11 @@ "stats", "utils" ], - "Hash": "d9c655b30a2edc6bb2244c1d1e8d549d" + "Hash": "8c7115cd3a0e048bda2a7cd110549f7a" }, "QuickJSR": { "Package": "QuickJSR", - "Version": "1.0.9", + "Version": "1.1.0", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -144,7 +144,7 @@ "Rcpp", "jsonlite" ], - "Hash": "1965c66cbe09d7878d3a8fa6ecbd8e71" + "Hash": "c2d96d1e0499a9f7e1e56d2d1dde0fa6" }, "R.methodsS3": { "Package": "R.methodsS3", @@ -159,7 +159,7 @@ }, "R.oo": { "Package": "R.oo", - "Version": "1.25.0", + "Version": "1.26.0", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -168,7 +168,7 @@ "methods", "utils" ], - "Hash": "a0900a114f4f0194cf4aa8cd4a700681" + "Hash": "4fed809e53ddb5407b3da3d0f572e591" }, "R.utils": { "Package": "R.utils", @@ -207,14 +207,14 @@ }, "Rcpp": { "Package": "Rcpp", - "Version": "1.0.11", + "Version": "1.0.12", "Source": "Repository", "Repository": "CRAN", "Requirements": [ "methods", "utils" ], - "Hash": "ae6cbbe1492f4de79c45fce06f967ce8" + "Hash": "5ea2700d21e038ace58269ecdbeb9ec0" }, "RcppEigen": { "Package": "RcppEigen", @@ -241,7 +241,7 @@ }, "StanHeaders": { "Package": "StanHeaders", - "Version": "2.26.28", + "Version": "2.32.5", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -249,7 +249,7 @@ "RcppEigen", "RcppParallel" ], - "Hash": "35b5aee9ec9507aca2e021997a9e557e" + "Hash": "b8d6850ef3e330bc108e712679e79443" }, "askpass": { "Package": "askpass", @@ -409,14 +409,14 @@ }, "cli": { "Package": "cli", - "Version": "3.6.2", + "Version": "3.6.1", "Source": "Repository", "Repository": "CRAN", "Requirements": [ "R", "utils" ], - "Hash": "1216ac65ac55ec0058a6f75d7ca0fd52" + "Hash": "89e6d8219950eac806ae0c489052048a" }, "clipr": { "Package": "clipr", @@ -509,14 +509,14 @@ }, "data.table": { "Package": "data.table", - "Version": "1.14.10", + "Version": "1.14.8", "Source": "Repository", "Repository": "CRAN", "Requirements": [ "R", "methods" ], - "Hash": "6ea17a32294d8ca00455825ab0cf71b9" + "Hash": "b4c06e554f33344e044ccd7fdca750a9" }, "dbplyr": { "Package": "dbplyr", @@ -653,14 +653,14 @@ }, "digest": { "Package": "digest", - "Version": "0.6.33", + "Version": "0.6.34", "Source": "Repository", "Repository": "CRAN", "Requirements": [ "R", "utils" ], - "Hash": "b18a9cf3c003977b0cc49d5e76ebe48d" + "Hash": "7ede2ee9ea8d3edbf1ca84c1e333ad1a" }, "distcrete": { "Package": "distcrete", @@ -768,11 +768,11 @@ "Version": "0.0.0.9000", "Source": "GitHub", "RemoteType": "github", - "RemoteHost": "api.github.com", "RemoteUsername": "epiverse-trace", "RemoteRepo": "epidemics", "RemoteRef": "main", - "RemoteSha": "5f6825d7ee9fbd162b26db95bc2f3e0d3054f452", + "RemoteSha": "14b38b8ffbda1ddfdf1d90b2d26d6570e1830d89", + "RemoteHost": "api.github.com", "Requirements": [ "BH", "Rcpp", @@ -785,7 +785,7 @@ "stats", "utils" ], - "Hash": "5abca47c2b33eb1a73a412a045823a8f" + "Hash": "8dd3418be52e67270b62ae55a5ef6b6c" }, "epiparameter": { "Package": "epiparameter", @@ -820,7 +820,7 @@ }, "fansi": { "Package": "fansi", - "Version": "1.0.6", + "Version": "1.0.5", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -828,7 +828,7 @@ "grDevices", "utils" ], - "Hash": "962174cf2aeb5b9eea581522286a911f" + "Hash": "3e8583a60163b4bc1a80016e63b9959e" }, "farver": { "Package": "farver", @@ -951,7 +951,7 @@ }, "future": { "Package": "future", - "Version": "1.33.0", + "Version": "1.33.1", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -962,11 +962,11 @@ "parallelly", "utils" ], - "Hash": "8e92c7bc53e91b9bb1faf9a6ef0e8514" + "Hash": "e57e292737f7a4efa9d8a91c5908222c" }, "future.apply": { "Package": "future.apply", - "Version": "1.11.0", + "Version": "1.11.1", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -976,7 +976,7 @@ "parallel", "utils" ], - "Hash": "ba4be138fe47eac3e16a6deaa4da106e" + "Hash": "455e00c16ec193c8edcf1b2b522b3288" }, "gargle": { "Package": "gargle", @@ -1049,14 +1049,14 @@ }, "glue": { "Package": "glue", - "Version": "1.6.2", + "Version": "1.7.0", "Source": "Repository", - "Repository": "RSPM", + "Repository": "CRAN", "Requirements": [ "R", "methods" ], - "Hash": "4f2596dfb05dac67b9dc558e5c6fba2e" + "Hash": "e0b3a53876554bd45879e596cdb10a52" }, "googledrive": { "Package": "googledrive", @@ -1357,13 +1357,13 @@ }, "jsonlite": { "Package": "jsonlite", - "Version": "1.8.8", + "Version": "1.8.7", "Source": "Repository", "Repository": "CRAN", "Requirements": [ "methods" ], - "Hash": "e1b9c55281c5adc4dd113652d9e26768" + "Hash": "266a20443ca13c65688b2116d5220f76" }, "knitr": { "Package": "knitr", @@ -1715,7 +1715,7 @@ }, "patchwork": { "Package": "patchwork", - "Version": "1.1.3", + "Version": "1.2.0", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -1729,7 +1729,7 @@ "stats", "utils" ], - "Hash": "c5754106c02e8e019941100c81149431" + "Hash": "9c8ab14c00ac07e9e04d1664c0b74486" }, "pillar": { "Package": "pillar", @@ -1776,6 +1776,27 @@ ], "Hash": "01f28d4278f15c76cddbea05899c5d6f" }, + "pkgload": { + "Package": "pkgload", + "Version": "1.3.4", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R", + "cli", + "crayon", + "desc", + "fs", + "glue", + "methods", + "pkgbuild", + "rlang", + "rprojroot", + "utils", + "withr" + ], + "Hash": "876c618df5ae610be84356d5d7a5d124" + }, "plyr": { "Package": "plyr", "Version": "1.8.9", @@ -1799,7 +1820,7 @@ }, "processx": { "Package": "processx", - "Version": "3.8.3", + "Version": "3.8.2", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -1808,7 +1829,7 @@ "ps", "utils" ], - "Hash": "82d48b1aec56084d9438dbf98087a7e9" + "Hash": "3efbd8ac1be0296a46c55387aeace0f3" }, "progress": { "Package": "progress", @@ -1838,14 +1859,14 @@ }, "ps": { "Package": "ps", - "Version": "1.7.5", + "Version": "1.7.6", "Source": "Repository", "Repository": "CRAN", "Requirements": [ "R", "utils" ], - "Hash": "709d852d33178db54b17c722e5b1e594" + "Hash": "dd2b9319ee0656c8acf45c7f40c59de7" }, "purrr": { "Package": "purrr", @@ -1885,7 +1906,7 @@ }, "readr": { "Package": "readr", - "Version": "2.1.4", + "Version": "2.1.5", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -1904,7 +1925,7 @@ "utils", "vroom" ], - "Hash": "b5047343b3825f37ad9d3b5d89aa1078" + "Hash": "9de96463d2117f6ac49980577939dfb3" }, "readxl": { "Package": "readxl", @@ -1940,7 +1961,7 @@ }, "reprex": { "Package": "reprex", - "Version": "2.0.2", + "Version": "2.1.0", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -1958,18 +1979,18 @@ "utils", "withr" ], - "Hash": "d66fe009d4c20b7ab1927eb405db9ee2" + "Hash": "1425f91b4d5d9a8f25352c44a3d914ed" }, "rlang": { "Package": "rlang", - "Version": "1.1.2", + "Version": "1.1.3", "Source": "Repository", "Repository": "CRAN", "Requirements": [ "R", "utils" ], - "Hash": "50a6dbdc522936ca35afc5e2082ea91b" + "Hash": "42548638fae05fd9a9b5f3f437fbbbe2" }, "rmarkdown": { "Package": "rmarkdown", @@ -2007,7 +2028,7 @@ }, "rstan": { "Package": "rstan", - "Version": "2.32.3", + "Version": "2.32.5", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -2026,7 +2047,7 @@ "pkgbuild", "stats4" ], - "Hash": "4ac5b7639d28cd4fab19baaf46f33c6a" + "Hash": "378a10b6373822761ec78021c105b059" }, "rstantools": { "Package": "rstantools", @@ -2219,16 +2240,16 @@ }, "stringi": { "Package": "stringi", - "Version": "1.8.3", + "Version": "1.8.2", "Source": "Repository", - "Repository": "https://carpentries.r-universe.dev", + "Repository": "CRAN", "Requirements": [ "R", "stats", "tools", "utils" ], - "Hash": "058aebddea264f4c99401515182e656a" + "Hash": "e68c45f81639001af5f1b15cd3599bbd" }, "stringr": { "Package": "stringr", @@ -2298,7 +2319,7 @@ }, "tidyr": { "Package": "tidyr", - "Version": "1.3.0", + "Version": "1.3.1", "Source": "Repository", "Repository": "CRAN", "Requirements": [ @@ -2317,7 +2338,7 @@ "utils", "vctrs" ], - "Hash": "e47debdc7ce599b070c8e78e8ac0cfcf" + "Hash": "915fb7ce036c22a6a33b5a8adb712eb1" }, "tidyselect": { "Package": "tidyselect", @@ -2377,14 +2398,14 @@ }, "timechange": { "Package": "timechange", - "Version": "0.2.0", + "Version": "0.3.0", "Source": "Repository", "Repository": "CRAN", "Requirements": [ "R", "cpp11" ], - "Hash": "8548b44f79a35ba1791308b61e6012d7" + "Hash": "c5f3c201b931cd6474d17d8700ccb1c8" }, "sys": { "Package": "sys", @@ -2466,13 +2487,13 @@ }, "uuid": { "Package": "uuid", - "Version": "1.1-1", + "Version": "1.2-0", "Source": "Repository", "Repository": "CRAN", "Requirements": [ "R" ], - "Hash": "3d78edfb977a69fc7a0341bee25e163f" + "Hash": "303c19bfd970bece872f93a824e323d9" }, "truncnorm": { "Package": "truncnorm", @@ -2590,16 +2611,15 @@ }, "withr": { "Package": "withr", - "Version": "2.5.2", + "Version": "3.0.0", "Source": "Repository", "Repository": "CRAN", "Requirements": [ "R", "grDevices", - "graphics", - "stats" + "graphics" ], - "Hash": "4b25e70111b7d644322e9513f403a272" + "Hash": "d31b6c62c10dcf11ec530ca6b0dd5d35" }, "wpp2017": { "Package": "wpp2017", @@ -2683,10 +2703,10 @@ }, "yaml": { "Package": "yaml", - "Version": "2.3.8", + "Version": "2.3.7", "Source": "Repository", "Repository": "CRAN", - "Hash": "29240487a071f535f5e5d5a323b7afbd" + "Hash": "0d0056cc5383fbc240ccd0cb584bf436" } } } From 183e2bd35f836132b60b52b0675214d539c5d7cb Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 25 Jan 2024 15:00:01 +0000 Subject: [PATCH 123/130] install RcppParallel from RSPM to renv.lock this follows the suggestion in https://github.com/epiverse-trace/tutorials/issues/111#issuecomment-1910127141 --- episodes/install.R | 1 - 1 file changed, 1 deletion(-) delete mode 100644 episodes/install.R diff --git a/episodes/install.R b/episodes/install.R deleted file mode 100644 index 3af55f94..00000000 --- a/episodes/install.R +++ /dev/null @@ -1 +0,0 @@ -install.packages("RcppParallel") From 6ceda33281c4550e23662a462d0d2766d6db5dde Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 1 Feb 2024 10:43:42 +0000 Subject: [PATCH 124/130] fix lintr warnings and move hexwall code to issue refer to https://github.com/epiverse-trace/tutorials/issues/118 --- episodes/introduction.Rmd | 28 ++-------------------------- 1 file changed, 2 insertions(+), 26 deletions(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index 440a5f6e..d3b1a504 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -85,9 +85,9 @@ Not all primary cases have the same probability of generating a secondary case. ```{r} generation_time <- dist_spec( - mean = 3.6, + mean = 3.6, sd = 3.1, - max = 20, + max = 20, distribution = "lognormal" ) ``` @@ -158,30 +158,6 @@ Epiverse-TRACE's aim is to provide a software ecosystem for outbreak analytics. ![](fig/pkgs-hexlogos.png) -```{r, results='asis', echo = FALSE, eval=FALSE} -# eval = TRUE -# turn on if maintainers manage to -# adapt css from main page to varnish -# pkg_list <- c("readepi","cleanepi","linelist","epiparameter","episoap") - -# from https://github.com/epiverse-trace/epiverse-trace.github.io/blob/main/index.qmd - -pkg_path <- "https://raw.githubusercontent.com/epiverse-trace/epiverse-trace.github.io/main/_data/epiverse_pkgs.csv" -hexwall_path<- "https://raw.githubusercontent.com/epiverse-trace/epiverse-trace.github.io/main/_templates/hexwall_element.Rmd" - -file.path(pkg_path) |> - read.csv() |> - # dplyr::filter( - # magrittr::is_in(pkgname,pkg_list)) |> - as.list() |> - purrr::transpose() |> - purrr::map_chr(~ knitr::knit_expand( - file = hexwall_path, - pkgname = .x$pkgname, - description = .x$description)) |> - cat(sep = "\n") -``` - ## How can I start? Our plan for these tutorials is to introduce key solutions from packages in all the tasks before and after the _Quantify transmission_ task, plus the required theory concepts to interpret modelling outputs and make rigorous conclusions. From e433db07c3e0b638cb7316364d40feda1ce594cd Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 1 Feb 2024 11:09:39 +0000 Subject: [PATCH 125/130] fix partially lintr warnings --- episodes/read-delays.Rmd | 74 ++++++++++++++++++++-------------------- 1 file changed, 37 insertions(+), 37 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 1b7e4d26..27261c82 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -293,7 +293,7 @@ epiparameter::epidist_db( disease = "covid", epi_dist = "serial", author = "Hiroshi", - single_epidist = T + single_epidist = TRUE ) ``` @@ -316,12 +316,12 @@ Now, we have an epidemiological parameter we can reuse! We can replace the numbe Let's assign this `` class object to the `covid_serialint` object. ```{r} -covid_serialint <- +covid_serialint <- epiparameter::epidist_db( disease = "covid", epi_dist = "serial", author = "Nishiura", - single_epidist = T + single_epidist = TRUE ) covid_serialint @@ -365,7 +365,7 @@ epiparameter::epidist_db( epiparameter::epidist_db( disease = "ebola", epi_dist = "incubation", - single_epidist = T + single_epidist = TRUE ) ``` @@ -380,7 +380,7 @@ To access the `sample_size`, review an [issue reported in the GitHub repository] We can get the `mean` and standard deviation (`sd`) from this `` diving into the `summary_stats` object: ```{r} -# get the mean +# get the mean covid_serialint$summary_stats$mean ``` @@ -416,7 +416,7 @@ Use the `str()` to display the structure of the `` R object. :::::::::: solution ```{r,eval=FALSE} -# get the sd +# get the sd covid_serialint$summary_stats$sd # get the sample_size @@ -460,9 +460,9 @@ An informative delay measures the time from symptom onset to recovery or death. ```{r,eval=FALSE} # one way to get the list of all the available parameters -epidist_db(disease = "all") %>% - list_distributions() %>% - as_tibble() %>% +epidist_db(disease = "all") %>% + list_distributions() %>% + as_tibble() %>% distinct(epi_distribution) ebola_severity <- epidist_db( @@ -488,13 +488,13 @@ ebola_severity$summary_stats$mean_ci_limits The following output has four entries with different content in the **probability distribution** (`prob_distribution`) column: ```{r} -distribution <- +distribution <- epiparameter::epidist_db( disease = "covid", epi_dist = "serial" ) -distribution %>% +distribution %>% list_distributions() ``` @@ -522,7 +522,7 @@ distribution[[4]]$metadata$inference_method In the `epiparameter::list_distributions()` output, we can also find different types of probability distributions (e.g., Log-normal, Weibull, Normal). ```{r} -distribution %>% +distribution %>% list_distributions() ``` @@ -601,7 +601,8 @@ cdf(covid_serialint, q = 10) # the quantile value (day) at a cumulative probability of 60% quantile(covid_serialint, p = 0.6) -# generate 10 random values (days) given the distribution family and its parameters +# generate 10 random values (days) given +# the distribution family and its parameters generate(covid_serialint, times = 10) ``` @@ -609,12 +610,10 @@ generate(covid_serialint, times = 10) Access to the reference documentation (Help files) for these functions is accessible with the three double-colon notation: `epiparameter:::` -```{r} -?epiparameter:::density.epidist() -?epiparameter:::cdf.epidist() -?epiparameter:::quantile.epidist() -?epiparameter:::generate.epidist() -``` +- `?epiparameter:::density.epidist()` +- `?epiparameter:::cdf.epidist()` +- `?epiparameter:::quantile.epidist()` +- `?epiparameter:::generate.epidist()` :::::::::::::::::: @@ -641,8 +640,8 @@ plot(covid_serialint) ``` ```{r,eval=FALSE} -cdf(covid_serialint,q = 2) -cdf(covid_serialint,q = 6) +cdf(covid_serialint, q = 2) +cdf(covid_serialint, q = 6) ``` Given the COVID-19 serial interval: @@ -686,7 +685,7 @@ We can use the set of distribution functions for a _continuous_ distribution (as When we `epiparameter::discretise()` the continuous distribution we get a **discrete**(-ized) distribution: ```{r} -covid_serialint_discrete <- +covid_serialint_discrete <- epiparameter::discretise(covid_serialint) covid_serialint_discrete @@ -711,7 +710,7 @@ plot(covid_serialint_discrete) To finally get a `max` value, let's access the quantile value of the 99.9th percentile or `0.999` probability of the distribution with the `prob_dist$q` notation, similarly to how we access the `summary_stats` values. ```{r} -covid_serialint_discrete_max <- +covid_serialint_discrete_max <- covid_serialint_discrete$prob_dist$q(p = 0.999) ``` @@ -743,7 +742,8 @@ covid_serialint_discrete$prob_dist$cdf(10) # In what quantile value (days) do we have the 60% cumulative probability? covid_serialint_discrete$prob_dist$q(0.6) -# covid_serialint_discrete$prob_dist$qf(0.6) # as in continuous distribution +# covid_serialint_discrete$prob_dist$qf(0.6) +# as in continuous distribution # generate random values covid_serialint_discrete$prob_dist$r(10) @@ -775,7 +775,7 @@ covid_incubation <- epiparameter::epidist_db( disease = "covid", epi_dist = "incubation", - single_epidist = T + single_epidist = TRUE ) covid_incubation_discrete <- epiparameter::discretise(covid_incubation) @@ -882,7 +882,7 @@ epidist_db(disease = "ebola") %>% ebola_serial <- epidist_db( disease = "ebola", epi_dist = "serial", - single_epidist = T + single_epidist = TRUE ) ebola_serial_discrete <- discretise(ebola_serial) @@ -969,7 +969,7 @@ covid_incubation <- epiparameter::epidist_db( disease = "covid", epi_dist = "incubation", author = "Natalie", - single_epidist = T + single_epidist = TRUE ) covid_incubation @@ -1055,7 +1055,7 @@ epidist_db(disease = "ebola") %>% ebola_serial <- epidist_db( disease = "ebola", epi_dist = "serial", - single_epidist = T + single_epidist = TRUE ) ebola_serial_discrete <- discretise(ebola_serial) @@ -1072,7 +1072,7 @@ serial_interval_ebola <- ebola_incubation <- epidist_db( disease = "ebola", epi_dist = "incubation", - single_epidist = T + single_epidist = TRUE ) ebola_incubation_discrete <- discretise(ebola_incubation) @@ -1264,22 +1264,22 @@ Key functions: ```{r,eval=FALSE} # pre-symptomatic transmission -epidist_db(disease = "influenza",epi_dist = "incubation",single_epidist = T)$summary_stats$mean -epidist_db(disease = "influenza",epi_dist = "serial",single_epidist = T)$summary_stats$mean +epidist_db(disease = "influenza",epi_dist = "incubation",single_epidist = TRUE)$summary_stats$mean +epidist_db(disease = "influenza",epi_dist = "serial",single_epidist = TRUE)$summary_stats$mean # symptomatic transmission -epidist_db(disease = "mers",epi_dist = "incubation",single_epidist = T)$summary_stats$median -epidist_db(disease = "mers",epi_dist = "serial",single_epidist = T)$summary_stats$mean +epidist_db(disease = "mers",epi_dist = "incubation",single_epidist = TRUE)$summary_stats$median +epidist_db(disease = "mers",epi_dist = "serial",single_epidist = TRUE)$summary_stats$mean ``` ```{r,eval=FALSE} # pre-symptomatic transmission -epidist_db(disease = "covid",epi_dist = "incubation",author = "Stephen",single_epidist = T)$summary_stats$mean -epidist_db(disease = "covid",epi_dist = "serial",author = "Nishiura",single_epidist = T)$summary_stats$mean +epidist_db(disease = "covid",epi_dist = "incubation",author = "Stephen",single_epidist = TRUE)$summary_stats$mean +epidist_db(disease = "covid",epi_dist = "serial",author = "Nishiura",single_epidist = TRUE)$summary_stats$mean # symptomatic transmission -epidist_db(disease = "ebola",epi_dist = "incubation",single_epidist = T)$summary_stats$mean -epidist_db(disease = "ebola",epi_dist = "serial",single_epidist = T)$summary_stats$mean +epidist_db(disease = "ebola",epi_dist = "incubation",single_epidist = TRUE)$summary_stats$mean +epidist_db(disease = "ebola",epi_dist = "serial",single_epidist = TRUE)$summary_stats$mean ``` :::::::::::::::::::::::::: From 52715dbcd5b9ec89309c5147dd92065f17f3c8bd Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Thu, 1 Feb 2024 21:27:49 +0000 Subject: [PATCH 126/130] edits to reduce lintr warnings --- episodes/read-delays.Rmd | 172 ++++++++++++++++++++++++++------------- 1 file changed, 114 insertions(+), 58 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 27261c82..9ba5f1f6 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -243,9 +243,9 @@ Take 5 minutes: The `{epiparameter}` combo of `epidist_db()` plus `list_distributions()` list all the entries by: -- disease, -- epidemiological distribution, -- the type of the probability distribution, +- disease, +- epidemiological distribution, +- the type of the probability distribution, - author of the study, and - year of study. @@ -340,10 +340,10 @@ Take 2 minutes: The `{epiparameter}` combo of `epidist_db()` plus `list_distributions()` list all the entries by: -- disease, -- epidemiological distribution, -- the type of the probability distribution, -- author of the study, and +- disease, +- epidemiological distribution, +- the type of the probability distribution, +- author of the study, and - year of study. This is a `` class object: @@ -742,8 +742,6 @@ covid_serialint_discrete$prob_dist$cdf(10) # In what quantile value (days) do we have the 60% cumulative probability? covid_serialint_discrete$prob_dist$q(0.6) -# covid_serialint_discrete$prob_dist$qf(0.6) -# as in continuous distribution # generate random values covid_serialint_discrete$prob_dist$r(10) @@ -752,12 +750,16 @@ covid_serialint_discrete$prob_dist$r(10) x_limit <- covid_serialint_discrete$prob_dist$q(0.999) tibble( - # generate quantile values as a sequence for each natural number - quantile_values = seq(1L, to = x_limit, by = 1L) -) %>% + # generate quantile values + # as a sequence for each natural number + quantile_values = + seq(1L, to = x_limit, by = 1L) +) %>% mutate( - # calculate the values for each quantile in the density function - density_values = covid_serialint_discrete$prob_dist$d(quantile_values) + # calculate the values + # for each quantile in the density function + density_values = + covid_serialint_discrete$prob_dist$d(quantile_values) ) %>% ggplot(aes( x = quantile_values, @@ -771,7 +773,7 @@ tibble( ::::::::::::::::: solution ```{r,eval=FALSE} -covid_incubation <- +covid_incubation <- epiparameter::epidist_db( disease = "covid", epi_dist = "incubation", @@ -797,7 +799,7 @@ _Is this result expected in epidemiological terms?_ Now we can plug everything into the `EpiNow2::dist_spec()` function! ```{r} -serial_interval_covid <- +serial_interval_covid <- dist_spec( mean = covid_serialint$summary_stats$mean, sd = covid_serialint$summary_stats$sd, @@ -838,15 +840,11 @@ base::plot(epinow_estimates) ### Ebola's effective reproduction number -Download the [Ebola dataset](data/ebola_cases.csv): +Download and read the [Ebola dataset](data/ebola_cases.csv): - Reuse one epidemiological parameter to estimate the effective reproduction number for the Ebola dataset. - Why did you choose that parameter? -```{r,eval=FALSE} -ebola_confirmed <- read_csv("data/raw-data/ebola_cases.csv") -``` - ::::::::::::::::: hint To calculate the $R_t$, we need: @@ -867,15 +865,18 @@ Key functions we applied in this episode are: ```{r,eval=FALSE,echo=FALSE} # read data from the tutorial repository R project -ebola_confirmed <- read_csv("episodes/data/ebola_cases.csv") +ebola_confirmed <- + read_csv(file.path("episodes","data","ebola_cases.csv")) ``` ```{r,message=FALSE,warning=FALSE,eval=FALSE} # read data -ebola_confirmed <- read_csv("data/raw-data/ebola_cases.csv") +# e.g.: if path to file is data/raw-data/ebola_cases.csv then: +ebola_confirmed <- + read_csv(here::here("data", "raw-data", "ebola_cases.csv")) # list distributions -epidist_db(disease = "ebola") %>% +epidist_db(disease = "ebola") %>% list_distributions() # subset one distribution @@ -887,7 +888,7 @@ ebola_serial <- epidist_db( ebola_serial_discrete <- discretise(ebola_serial) -serial_interval_ebola <- +serial_interval_ebola <- dist_spec( mean = ebola_serial$summary_stats$mean, sd = ebola_serial$summary_stats$sd, @@ -1026,10 +1027,6 @@ Using the same [Ebola dataset](data/ebola_cases.csv): - Estimate the effective reproduction number using `EpiNow2::epinow()`. - Why did you choose that parameter? -```{r,eval=FALSE} -ebola_confirmed <- read_csv("data/raw-data/ebola_cases.csv") -``` - ::::::::::::::::: hint We can use two complementary delay distributions to estimate the $R_t$ at time $t$. @@ -1040,21 +1037,23 @@ We can use two complementary delay distributions to estimate the $R_t$ at time $ ```{r,eval=FALSE,echo=FALSE} # read data from the tutorial repository R project -ebola_confirmed <- read_csv("episodes/data/ebola_cases.csv") +ebola_confirmed <- read_csv(file.path("episodes","data","ebola_cases.csv")) ``` ```{r,message=FALSE,warning=FALSE,eval=FALSE} # read data -ebola_confirmed <- read_csv("data/raw-data/ebola_cases.csv") +# e.g.: if path to file is data/raw-data/ebola_cases.csv then: +ebola_confirmed <- + read_csv(here::here("data", "raw-data", "ebola_cases.csv")) # list distributions -epidist_db(disease = "ebola") %>% +epidist_db(disease = "ebola") %>% list_distributions() -# subset one distribution for the generation time +# subset one distribution for the generation time ebola_serial <- epidist_db( - disease = "ebola", - epi_dist = "serial", + disease = "ebola", + epi_dist = "serial", single_epidist = TRUE ) @@ -1070,14 +1069,14 @@ serial_interval_ebola <- # subset one distribution for delay of the incubation period ebola_incubation <- epidist_db( - disease = "ebola", - epi_dist = "incubation", + disease = "ebola", + epi_dist = "incubation", single_epidist = TRUE ) ebola_incubation_discrete <- discretise(ebola_incubation) -incubation_period_ebola <- +incubation_period_ebola <- dist_spec( mean = ebola_incubation$summary_stats$mean, sd = ebola_incubation$summary_stats$sd, @@ -1127,9 +1126,9 @@ How to get the mean and standard deviation from a generation time with median an ```{r,eval=FALSE} # What parameters are available for Influenza? -epidist_db(disease = "influenza") %>% - list_distributions() %>% - as_tibble() %>% +epidist_db(disease = "influenza") %>% + list_distributions() %>% + as_tibble() %>% count(epi_distribution) influenza_generation <- epidist_db(disease = "influenza",epi_dist = "generation") @@ -1154,7 +1153,7 @@ influenza_extracted <- extract_param( influenza_extracted -generation_time_influenza <- +generation_time_influenza <- dist_spec( mean = influenza_extracted[1], sd = influenza_extracted[2], @@ -1162,7 +1161,7 @@ generation_time_influenza <- distribution = "lognormal" ) -influenza_cleaned <- outbreaks::influenza_england_1978_school %>% +influenza_cleaned <- outbreaks::influenza_england_1978_school %>% select(date,confirm = in_bed) epinow_estimates <- epinow( @@ -1180,13 +1179,14 @@ epinow_estimates <- epinow( plot(epinow_estimates) ``` -```{r,eval=FALSE,echo=FALSE} -# question: is dispersion valid for quantiles? -# convert_summary_stats_to_params( -# distribution = "lnorm", -# median = influenza_generation$summary_stats$median, -# quantiles = influenza_generation$summary_stats$quantiles) -``` + + + + + + + + :::::::::::::::::::::::::: @@ -1262,24 +1262,80 @@ Key functions: ::::::::::::::::: solution +In this solution we use `purrr::pluck()` to extract elements within the `summary_stats` object which is of class `list`. + ```{r,eval=FALSE} # pre-symptomatic transmission -epidist_db(disease = "influenza",epi_dist = "incubation",single_epidist = TRUE)$summary_stats$mean -epidist_db(disease = "influenza",epi_dist = "serial",single_epidist = TRUE)$summary_stats$mean +epidist_db( + disease = "influenza", + epi_dist = "incubation", + single_epidist = TRUE +) %>% + pluck("summary_stats") %>% + pluck("mean") + +epidist_db( + disease = "influenza", + epi_dist = "serial", + single_epidist = TRUE +) %>% + pluck("summary_stats") %>% + pluck("mean") # symptomatic transmission -epidist_db(disease = "mers",epi_dist = "incubation",single_epidist = TRUE)$summary_stats$median -epidist_db(disease = "mers",epi_dist = "serial",single_epidist = TRUE)$summary_stats$mean +epidist_db( + disease = "mers", + epi_dist = "incubation", + single_epidist = TRUE +) %>% + pluck("summary_stats") %>% + pluck("median") + +epidist_db( + disease = "mers", + epi_dist = "serial", + single_epidist = TRUE +) %>% + pluck("summary_stats") %>% + pluck("mean") ``` ```{r,eval=FALSE} # pre-symptomatic transmission -epidist_db(disease = "covid",epi_dist = "incubation",author = "Stephen",single_epidist = TRUE)$summary_stats$mean -epidist_db(disease = "covid",epi_dist = "serial",author = "Nishiura",single_epidist = TRUE)$summary_stats$mean +epidist_db( + disease = "covid", + epi_dist = "incubation", + author = "Stephen", + single_epidist = TRUE +) %>% + pluck("summary_stats") %>% + pluck("mean") + +epidist_db( + disease = "covid", + epi_dist = "serial", + author = "Nishiura", + single_epidist = TRUE +) %>% + pluck("summary_stats") %>% + pluck("mean") # symptomatic transmission -epidist_db(disease = "ebola",epi_dist = "incubation",single_epidist = TRUE)$summary_stats$mean -epidist_db(disease = "ebola",epi_dist = "serial",single_epidist = TRUE)$summary_stats$mean +epidist_db( + disease = "ebola", + epi_dist = "incubation", + single_epidist = TRUE +) %>% + pluck("summary_stats") %>% + pluck("mean") + +epidist_db( + disease = "ebola", + epi_dist = "serial", + single_epidist = TRUE +) %>% + pluck("summary_stats") %>% + pluck("mean") ``` :::::::::::::::::::::::::: From 8cb2253c17c05b35c847c1098d9ac9be37ecf6f3 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 2 Feb 2024 10:23:43 +0000 Subject: [PATCH 127/130] edits to reduce lintr warnings --- episodes/read-delays.Rmd | 39 +++++++++++++++++++++++---------------- 1 file changed, 23 insertions(+), 16 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 9ba5f1f6..4e3a87b2 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -601,7 +601,7 @@ cdf(covid_serialint, q = 10) # the quantile value (day) at a cumulative probability of 60% quantile(covid_serialint, p = 0.6) -# generate 10 random values (days) given +# generate 10 random values (days) given # the distribution family and its parameters generate(covid_serialint, times = 10) ``` @@ -865,24 +865,24 @@ Key functions we applied in this episode are: ```{r,eval=FALSE,echo=FALSE} # read data from the tutorial repository R project -ebola_confirmed <- - read_csv(file.path("episodes","data","ebola_cases.csv")) +ebola_confirmed <- + read_csv(file.path("episodes", "data", "ebola_cases.csv")) ``` ```{r,message=FALSE,warning=FALSE,eval=FALSE} # read data # e.g.: if path to file is data/raw-data/ebola_cases.csv then: -ebola_confirmed <- +ebola_confirmed <- read_csv(here::here("data", "raw-data", "ebola_cases.csv")) # list distributions epidist_db(disease = "ebola") %>% list_distributions() -# subset one distribution +# subset one distribution ebola_serial <- epidist_db( - disease = "ebola", - epi_dist = "serial", + disease = "ebola", + epi_dist = "serial", single_epidist = TRUE ) @@ -1037,13 +1037,14 @@ We can use two complementary delay distributions to estimate the $R_t$ at time $ ```{r,eval=FALSE,echo=FALSE} # read data from the tutorial repository R project -ebola_confirmed <- read_csv(file.path("episodes","data","ebola_cases.csv")) +ebola_confirmed <- + read_csv(file.path("episodes", "data", "ebola_cases.csv")) ``` ```{r,message=FALSE,warning=FALSE,eval=FALSE} # read data # e.g.: if path to file is data/raw-data/ebola_cases.csv then: -ebola_confirmed <- +ebola_confirmed <- read_csv(here::here("data", "raw-data", "ebola_cases.csv")) # list distributions @@ -1059,7 +1060,7 @@ ebola_serial <- epidist_db( ebola_serial_discrete <- discretise(ebola_serial) -serial_interval_ebola <- +serial_interval_ebola <- dist_spec( mean = ebola_serial$summary_stats$mean, sd = ebola_serial$summary_stats$sd, @@ -1067,7 +1068,7 @@ serial_interval_ebola <- distribution = "gamma" ) -# subset one distribution for delay of the incubation period +# subset one distribution for delay of the incubation period ebola_incubation <- epidist_db( disease = "ebola", epi_dist = "incubation", @@ -1131,9 +1132,14 @@ epidist_db(disease = "influenza") %>% as_tibble() %>% count(epi_distribution) -influenza_generation <- epidist_db(disease = "influenza",epi_dist = "generation") +influenza_generation <- + epidist_db( + disease = "influenza", + epi_dist = "generation" + ) -influenza_generation_discrete <- discretise(influenza_generation) +influenza_generation_discrete <- + discretise(influenza_generation) # problem # the summary statistics do not have mean and sd @@ -1148,7 +1154,7 @@ influenza_extracted <- extract_param( values = c(influenza_generation$summary_stats$quantiles[1], influenza_generation$summary_stats$quantiles[2]), distribution = "lnorm", - percentiles = c(0.05,0.95) + percentiles = c(0.05, 0.95) ) influenza_extracted @@ -1161,8 +1167,9 @@ generation_time_influenza <- distribution = "lognormal" ) -influenza_cleaned <- outbreaks::influenza_england_1978_school %>% - select(date,confirm = in_bed) +influenza_cleaned <- + outbreaks::influenza_england_1978_school %>% + select(date, confirm = in_bed) epinow_estimates <- epinow( # cases From 75702207bf8ef73735658bdf0ddf3d09b28c3162 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 2 Feb 2024 10:24:47 +0000 Subject: [PATCH 128/130] relocate hint as extension of solution challenge --- episodes/read-delays.Rmd | 56 ++++++++++++++++++++++++---------------- 1 file changed, 34 insertions(+), 22 deletions(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index 4e3a87b2..cfe7ba87 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -745,27 +745,6 @@ covid_serialint_discrete$prob_dist$q(0.6) # generate random values covid_serialint_discrete$prob_dist$r(10) - -# create a discrete distribution visualization -x_limit <- covid_serialint_discrete$prob_dist$q(0.999) - -tibble( - # generate quantile values - # as a sequence for each natural number - quantile_values = - seq(1L, to = x_limit, by = 1L) -) %>% - mutate( - # calculate the values - # for each quantile in the density function - density_values = - covid_serialint_discrete$prob_dist$d(quantile_values) - ) %>% - ggplot(aes( - x = quantile_values, - y = density_values) - ) + - geom_col() ``` :::::::::::::::::::::: @@ -787,7 +766,40 @@ covid_incubation_discrete$prob_dist$q(0.99) 99% of those who develop COVID-19 symptoms will do so within 16 days of infection. -_Is this result expected in epidemiological terms?_ +Now, _Is this result expected in epidemiological terms?_ + +:::::::::::::::::::::::::: + +::::::::::::::::: solution + +### how to create a distribution plot? + +From a maximum value with `$prob_dist$q()`, we can create a sequence of quantile values as a numeric vector and map density values for each: + +```{r} +# create a discrete distribution visualization +# from a maximum value from the distribution +covid_serialint_discrete$prob_dist$q(0.999) %>% + # generate quantile values + # as a sequence for each natural number + seq(1L, to = ., by = 1L) %>% + # coerce numeric vector to data frame + as_tibble_col(column_name = "quantile_values") %>% + mutate( + # map density values + # for each quantile in the density function + density_values = + covid_serialint_discrete$prob_dist$d(quantile_values) + ) %>% + # create plot + ggplot( + aes( + x = quantile_values, + y = density_values + ) + ) + + geom_col() +``` :::::::::::::::::::::::::: From 0866c264d71a8735f84d7cd146527c3e6ee3a657 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Fri, 2 Feb 2024 10:36:41 +0000 Subject: [PATCH 129/130] remove discrete distribution plot output --- episodes/read-delays.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/episodes/read-delays.Rmd b/episodes/read-delays.Rmd index cfe7ba87..90e6c8e3 100644 --- a/episodes/read-delays.Rmd +++ b/episodes/read-delays.Rmd @@ -776,7 +776,7 @@ Now, _Is this result expected in epidemiological terms?_ From a maximum value with `$prob_dist$q()`, we can create a sequence of quantile values as a numeric vector and map density values for each: -```{r} +```{r,eval=FALSE} # create a discrete distribution visualization # from a maximum value from the distribution covid_serialint_discrete$prob_dist$q(0.999) %>% From 6a5a730b7d5f14dd37ead9fe64b6fd042432d356 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Tue, 6 Feb 2024 10:57:09 +0000 Subject: [PATCH 130/130] remove cache from chunk options --- episodes/introduction.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/episodes/introduction.Rmd b/episodes/introduction.Rmd index d3b1a504..bf059f68 100644 --- a/episodes/introduction.Rmd +++ b/episodes/introduction.Rmd @@ -100,7 +100,7 @@ In the `epinow()` function we can add: - the `generation_time` delay distribution, and - the computation `stan` parameters for this calculation: -```{r,message=FALSE,warning=FALSE,cache=TRUE} +```{r,message=FALSE,warning=FALSE} epinow_estimates <- epinow( # cases reported_cases = example_confirmed[1:60],

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