From b3a8bdc3465187bdd128c923621295ec6e61b046 Mon Sep 17 00:00:00 2001 From: Andree Valle Campos Date: Mon, 8 Apr 2024 15:42:46 +0100 Subject: [PATCH] add edits from PR #19 --- episodes/delays-reuse.Rmd | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/episodes/delays-reuse.Rmd b/episodes/delays-reuse.Rmd index 3854a890..1c26286d 100644 --- a/episodes/delays-reuse.Rmd +++ b/episodes/delays-reuse.Rmd @@ -95,7 +95,7 @@ epinow_estimates <- epinow( ``` --> -## Find a Generation time +## Generation time vs serial interval The generation time, jointly with the reproduction number ($R$), provide valuable insights on the strength of transmission and inform the implementation of control measures. Given a $R>1$, the shorter the generation time, the earlier the incidence of disease cases will grow. @@ -108,7 +108,7 @@ This frequent approximation is because it is easier to observe and measure the o 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 for diseases with 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 differs, propagating bias to the $R_{t}$ estimation ([Gostic et al., 2020](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008409)). + ::::::::::::::::: callout @@ -188,7 +188,7 @@ The objective of the assessment above is to assess the interpretation of a large In this section, we will use `{epiparameter}` to obtain the generation time and the serial interval for COVID-19, so these metrics can be used to estimate the transmissibility of this disease using `{EpiNow2}` in subsequent sections of this episode. -Let's start by looking at how many parameters we have in the epidemiological distributions database in `{epiparameter}` (`epidist_db`) for the `disease` named `covid`-19: +Let's start by looking at how many entries are available in the epidemiological distributions database in `{epiparameter}` (`epidist_db`) for the `disease` named `covid`-19: ```{r} epiparameter::epidist_db( @@ -206,7 +206,7 @@ epiparameter::epidist_db( ) ``` -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. Run this locally! +Currently, in the library of epidemiological parameters, we have one `generation` time entry for Influenza. Considering the above-mentioned considerations, we can look at the `serial` intervals for `COVID`-19. Run this locally! ```{r,eval=FALSE} epiparameter::epidist_db( @@ -221,7 +221,7 @@ With this query combination, we get more than one delay distribution. This outpu ### 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. +`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. Strings like `"serial"`, `"serial interval"` or `"serial_interval"` are valid. ::::::::::::::::::::::::: @@ -400,8 +400,6 @@ What is a *parametrised* ``? Look at `?is_parameterised`. ::::::::::::::::::::::::: -Now, we have an epidemiological parameter we can reuse! We can replace the **summary statistics** numbers we plug into `EpiNow2::dist_spec()`. - Let's assign this `` class object to the `covid_serialint` object. ```{r,message=FALSE} @@ -465,7 +463,7 @@ We can get the `mean` and standard deviation (`sd`) from this `` diving covid_serialint$summary_stats$mean ``` -Notice that with this output we can replace one of the inputs for the `EpiNow2::dist_spec()` function: +Now, we have an epidemiological parameter we can reuse! We can replace the **summary statistics** numbers we plug into the `EpiNow2::dist_spec()` function: ```r generation_time <-