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CODE_OF_CONDUCT.md

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+---
+title: "Contributor Code of Conduct"
+---
+
+As contributors and maintainers of this project,
+we pledge to follow the [The Epiverse-TRACE Code of Conduct][coc].
+
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+[coc-reporting]: https://forms.gle/guKqVXPk6K43jPn59
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+

LICENSE.md

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+---
+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).
+
+All Epiverse-TRACE
+instructional material is made available under the [Creative Commons
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+license][cc-by-legal].
+
+You are free:
+
+- to **Share**---copy and redistribute the material in any medium or format
+- to **Adapt**---remix, transform, and build upon the material
+
+for any purpose, even commercially.
+
+The licensor cannot revoke these freedoms as long as you follow the license
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+
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+
+- **No additional restrictions**---You may not apply legal terms or
+  technological measures that legally restrict others from doing anything the
+  license permits.  With the understanding that:
+
+Notices:
+
+* You do not have to comply with the license for elements of the material in
+  the public domain or where your use is permitted by an applicable exception
+  or limitation.
+* No warranties are given. The license may not give you all of the permissions
+  necessary for your intended use. For example, other rights such as publicity,
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+
+## Software
+
+Except where otherwise noted, the example programs and other software provided
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+[mit-license]: https://opensource.org/licenses/mit-license.html
+[osi]: https://opensource.org

compare-interventions.md

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+---
+title: 'Comparing public health outcomes of interventions'
+teaching: 45 # teaching time in minutes
+exercises: 30 # exercise time in minutes
+
+---
+
+:::::::::::::::::::::::::::::::::::::: questions 
+
+- How can I quantify the effect of an intervention?
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::: objectives
+
+- Compare intervention scenarios
+
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::: prereq
+
+## Prerequisites
++ Complete tutorials [Simulating transmission](../episodes/simulating-transmission.md) and [Modelling interventions](../episodes/modelling-interventions.md)
+
+Learners should familiarise themselves with following concept dependencies before working through this tutorial: 
+
+**Outbreak response** : [Intervention types](https://www.cdc.gov/nonpharmaceutical-interventions/).
+:::::::::::::::::::::::::::::::::
+
+
+## Introduction
+
+In this tutorial we will compare intervention scenarios against each other. To quantify the effect of the intervention we need to compare our intervention scenario to a counter factual scenario. The *counter factual* is the scenario in which nothing changes, often referred to as the 'do nothing' scenario. The counter factual scenario may include no interventions, or if we are investigating the potential impact of an additional intervention in the later stages of an outbreak there may be existing interventions in place. 
+
+We must also decide what our *outcome of interest* is to make comparisons between intervention and counter factual scenarios. The outcome of interest can be:
+
++ a model outcome, e.g. number of infections or hospitalisations,
++ a metric such as the epidemic peak time or size,
++ a measure that uses the model outcomes such as QALY/DALYs.
+
+
+:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: instructor
+
+In this tutorial we introduce the concept of the counter factual and how to compare scenarios (counter factual versus intervention) against each other. 
+
+::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Vacamole model
+
+The Vacamole model is a deterministic model based on a system of ODEs in [Ainslie et al. 2022]( https://doi.org/10.2807/1560-7917.ES.2022.27.44.2101090) to describe the effect of vaccination on COVID-19 dynamics. The model consists of 11 compartments, individuals are classed as one of the following:
+
++ susceptible, $S$,
++ partial vaccination ($V_1$), fully vaccination ($V_2$),
++ exposed, $E$ and exposed while vaccinated, $E_V$,
++ infectious, $I$ and infectious while vaccinated, $I_V$,
++ hospitalised, $H$ and hospitalised while vaccinated, $H_V$,
++ dead, $D$,
++ recovered, $R$.
+
+The diagram below describes the flow of individuals through the different compartments. 
+
+<img src="fig/compare-interventions-rendered-unnamed-chunk-1-1.png" style="display: block; margin: auto;" />
+
+
+::::::::::::::::::::::::::::::::::::: challenge
+
+## Running a counterfactual scenario using the Vacamole model 
+
+1. Run the model with the default parameter values for the UK population assuming that :
+
++ 1 in a million individual are infectious (and not vaccinated) at the start of the simulation
++ The contact matrix for the United Kingdom has age bins:
+  + age between 0 and 20 years,
+  + age between 20 and 40,
+  + 40 years and over.
++ There is no vaccination scheme in place
+
+2. Using the output, plot the number of deaths through time
+
+
+::::::::::::::::: hint
+
+### Vaccination code 
+
+To run the model with no vaccination in place we can *either* create two vaccination objects (one for each dose) using `vaccination()` with the time start, time end and vaccination rate all set to 0, or we can use the `no_vaccination()` function to create a vaccination object for two doses with all values set to 0.
+
+
+```r
+no_vaccination <- no_vaccination(population = uk_population, doses = 2)
+```
+::::::::::::::::::::::
+
+::::::::::::::::: hint
+
+### HINT : Running the model with default parameter values
+
+We can run the Vacamole model with [default parameter values](https://epiverse-trace.github.io/epidemics/articles/vacamole.html#model-epidemic-using-vacamole) by just specifying the population object and number of time steps to run the model for: 
+
+
+
+```r
+output <- model_vacamole_cpp(
+  population = uk_population,
+  vaccination = no_vaccination,
+  time_end = 300
+)
+```
+
+::::::::::::::::::::::
+
+
+
+::::::::::::::::: solution
+
+### SOLUTION
+
+1.  Run the model
+
+
+```r
+polymod <- socialmixr::polymod
+contact_data <- socialmixr::contact_matrix(
+  survey = polymod,
+  countries = "United Kingdom",
+  age.limits = c(0, 20, 40),
+  symmetric = TRUE
+)
+```
+
+```{.output}
+Using POLYMOD social contact data. To cite this in a publication, use the 'get_citation()' function
+```
+
+```{.output}
+Removing participants that have contacts without age information. To change this behaviour, set the 'missing.contact.age' option
+```
+
+```r
+# prepare contact matrix
+contact_matrix <- t(contact_data$matrix)
+
+# extract demography vector
+demography_vector <- contact_data$demography$population
+names(demography_vector) <- rownames(contact_matrix)
+
+# prepare initial conditions
+initial_i <- 1e-6
+
+initial_conditions <- c(
+  S = 1 - initial_i,
+  V1 = 0, V2 = 0,
+  E = 0, EV = 0,
+  I = initial_i, IV = 0,
+  H = 0, HV = 0, D = 0, R = 0
+)
+
+initial_conditions <- rbind(
+  initial_conditions,
+  initial_conditions,
+  initial_conditions
+)
+rownames(initial_conditions) <- rownames(contact_matrix)
+
+# prepare population object
+uk_population <- population(
+  name = "UK",
+  contact_matrix = contact_matrix,
+  demography_vector = demography_vector,
+  initial_conditions = initial_conditions
+)
+```
+
+```{.error}
+Error in population(name = "UK", contact_matrix = contact_matrix, demography_vector = demography_vector, : could not find function "population"
+```
+
+```r
+no_vaccination <- no_vaccination(population = uk_population, doses = 2)
+```
+
+```{.error}
+Error in no_vaccination(population = uk_population, doses = 2): could not find function "no_vaccination"
+```
+
+```r
+# run model
+output <- model_vacamole_cpp(
+  population = uk_population,
+  vaccination = no_vaccination,
+  time_end = 300
+)
+```
+
+```{.error}
+Error in model_vacamole_cpp(population = uk_population, vaccination = no_vaccination, : could not find function "model_vacamole_cpp"
+```
+
+2. Plot the number of deaths through time
+
+
+```r
+ggplot(output[output$compartment == "dead", ]) +
+  geom_line(
+    aes(time, value, colour = demography_group),
+    linewidth = 1
+  ) +
+  scale_colour_brewer(
+    palette = "Dark2",
+    labels = rownames(contact_matrix),
+    name = "Age group"
+  ) +
+  scale_y_continuous(
+    labels = scales::comma
+  ) +
+  labs(
+    x = "Simulation time (days)",
+    y = "Individuals"
+  ) +
+  theme(
+    legend.position = "top"
+  ) +
+  theme_bw(
+    base_size = 15
+  )
+```
+
+```{.error}
+Error in ggplot(output[output$compartment == "dead", ]): could not find function "ggplot"
+```
+
+
+
+:::::::::::::::::::::::::::
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+
+
+## Comparing scenarios
+
+*Coming soon*
+
+
+
+## Challenge : Ebola outbreak analysis 
+
+*Coming soon*
+
+
+
+
+::::::::::::::::::::::::::::::::::::: keypoints 
+
+- The counter factual scenario must be defined to make comparisons
+
+::::::::::::::::::::::::::::::::::::::::::::::::