Updates socialmixr interface using survey_pop and wpp2024

episodes/compare-interventions.Rmd

@@ -8,6 +8,9 @@ exercises: 30 # exercise time in minutes
 ```{r setup, echo= FALSE, message = FALSE, warning = FALSE}
 webshot::install_phantomjs(force = TRUE)
 library(epidemics)
+library(contactsurveys)
+library(wpp2024)
+library(tidyverse)
 
 # hidden seed for stable stochastic output in lesson
 set.seed(33)
@@ -18,12 +21,19 @@ survey_files <- contactsurveys::download_survey(
 )
 survey_load <- socialmixr::load_survey(files = survey_files)
 
+data(popAge1dt, package = "wpp2024")
+
+uk_pop <- popAge1dt %>%
+  dplyr::filter(name == "United Kingdom", year == max(year)) %>%
+  dplyr::select(lower.age.limit = age, population = pop) %>%
+  dplyr::mutate(population = population * 1000)
+
 contacts_byage <- socialmixr::contact_matrix(
   survey = survey_load,
   countries = "United Kingdom",
   age_limits = c(0, 15, 65),
   symmetric = TRUE,
-  return_demography = TRUE
+  survey_pop = uk_pop
 )
 
 # prepare contact matrix
@@ -344,12 +354,19 @@ survey_files_uk <- contactsurveys::download_survey(
 )
 survey_load_uk <- socialmixr::load_survey(files = survey_files_uk)
 
+data(popAge1dt, package = "wpp2024")
+
+uk_pop <- popAge1dt %>%
+  dplyr::filter(name == "United Kingdom", year == max(year)) %>%
+  dplyr::select(lower.age.limit = age, population = pop) %>%
+  dplyr::mutate(population = population * 1000)
+
 contacts_byage_uk <- socialmixr::contact_matrix(
   survey = survey_load_uk,
   countries = "United Kingdom",
   age_limits = c(0, 20, 40),
   symmetric = TRUE,
-  return_demography = TRUE
+  survey_pop = uk_pop
 )
 # prepare contact matrix
 contacts_byage_matrix_uk <- t(contacts_byage_uk$matrix)

episodes/contact-matrices.Rmd

@@ -34,12 +34,16 @@ webshot::install_phantomjs(force = TRUE)
 
 ## Introduction
 
-Some groups of individuals have more contacts than others; the average schoolchild has many more daily contact than the average elderly person, for example. This heterogeneity of contact patterns between different groups can affect disease transmission, because certain groups are more likely to transmit to others within that group, as well as to other groups. The rate at which individuals within and between groups make contact with others can be summarised in a contact matrix. In this tutorial we are going to learn how contact matrices can be used in different analyses and how the `{socialmixr}` package can be used to estimate contact matrices. 
+Some groups of individuals have more contacts than others; the average schoolchild has many more daily contact than the average elderly person, for example. This heterogeneity of contact patterns between different groups can affect disease transmission, because certain groups are more likely to transmit to others within that group, as well as to other groups. The rate at which individuals within and between groups make contact with others can be summarised in a contact matrix. 
+
+In this tutorial we are going to learn how contact matrices can be used in different analyses, how the package `{contactsurveys}` can be used to access survey data from different countries, and how the `{socialmixr}` package can be used to estimate contact matrices. We'll use `{dplyr}`, `{ggplot2}` and the pipe `%>%` to connect some of their functions, so let's also call to the `{tidyverse}` package:
 
 
 ```{r,message=FALSE,warning=FALSE}
 library(contactsurveys)
 library(socialmixr)
+library(wpp2024)
+library(tidyverse)
 ```
 
 ## The contact matrix
@@ -96,15 +100,22 @@ levels(survey_load$participants$country)
 
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
-We obtain the contact matrix for the United Kingdom — passing `countries = "United Kingdom"` to select data from the intended country, `age_limits` to define age categories, and `return_demography = TRUE` to include demographic information required by `{epidemics}`.
+We obtain the contact matrix for the United Kingdom — passing `countries = "United Kingdom"` to select data from the intended country, `age_limits` to define age categories, and `survey_pop` to supply the population structure from `{wpp2024}` required by `{socialmixr}`.
 
 ```{r polymod_uk, echo = TRUE}
+data(popAge1dt, package = "wpp2024")
+
+uk_pop <- popAge1dt %>%
+  dplyr::filter(name == "United Kingdom", year == max(year)) %>%
+  dplyr::select(lower.age.limit = age, population = pop) %>%
+  dplyr::mutate(population = population * 1000)
+
 contacts_byage <- socialmixr::contact_matrix(
   survey = survey_load,
   countries = "United Kingdom",
   age_limits = c(0, 20, 40),
   symmetric = TRUE,
-  return_demography = TRUE
+  survey_pop = uk_pop
 )
 contacts_byage
 ```
@@ -195,13 +206,20 @@ Similar to the code above, to access vector values within a dataframe, you can u
 :::::::::::::::::::::::: instructor 
 
 ```{r zambia_solution}
+data(popAge1dt, package = "wpp2024")
+
+zambia_pop <- popAge1dt %>%
+  dplyr::filter(name == "Zambia", year == max(year)) %>%
+  dplyr::select(lower.age.limit = age, population = pop) %>%
+  dplyr::mutate(population = population * 1000)
+
 # Generate the contact matrix for Zambia only
 contacts_byage_zambia <- socialmixr::contact_matrix(
   survey = survey_load_zambia,
   countries = "Zambia", # key argument
   age_limits = c(0, 20),
   symmetric = TRUE,
-  return_demography = TRUE
+  survey_pop = zambia_pop
 )
 
 # Print the contact matrix for Zambia only
@@ -315,6 +333,7 @@ contact_data_split <- socialmixr::contact_matrix(
   countries = "United Kingdom",
   age_limits = c(0, 20, 40),
   symmetric = TRUE,
+  survey_pop = uk_pop,
   split = TRUE
 )
 

episodes/disease-burden.Rmd

@@ -49,6 +49,7 @@ library(epiparameter)
 library(epidemics)
 library(contactsurveys)
 library(socialmixr)
+library(wpp2024)
 library(tidyverse)
 ```
 
@@ -91,12 +92,19 @@ survey_files <- contactsurveys::download_survey(
 )
 survey_load <- socialmixr::load_survey(files = survey_files)
 
+data(popAge1dt, package = "wpp2024")
+
+uk_pop <- popAge1dt %>%
+  dplyr::filter(name == "United Kingdom", year == max(year)) %>%
+  dplyr::select(lower.age.limit = age, population = pop) %>%
+  dplyr::mutate(population = population * 1000)
+
 contacts_byage <- socialmixr::contact_matrix(
   survey = survey_load,
   countries = "United Kingdom",
   age_limits = c(0, 20, 40),
   symmetric = TRUE,
-  return_demography = TRUE
+  survey_pop = uk_pop
 )
 
 # prepare contact matrix

episodes/modelling-interventions.Rmd

@@ -59,6 +59,7 @@ In this tutorial, we will learn how to use `{epidemics}` to model interventions
 library(epidemics)
 library(contactsurveys)
 library(socialmixr)
+library(wpp2024)
 library(tidyverse)
 ```
 
@@ -93,13 +94,20 @@ survey_files <- contactsurveys::download_survey(
 )
 survey_load <- socialmixr::load_survey(files = survey_files)
 
+data(popAge1dt, package = "wpp2024")
+
+uk_pop <- popAge1dt %>%
+  dplyr::filter(name == "United Kingdom", year == max(year)) %>%
+  dplyr::select(lower.age.limit = age, population = pop) %>%
+  dplyr::mutate(population = population * 1000)
+
 # generate contact matrix
 contacts_byage <- socialmixr::contact_matrix(
   survey = survey_load,
   countries = "United Kingdom",
   age_limits = c(0, 15, 65),
   symmetric = TRUE,
-  return_demography = TRUE
+  survey_pop = uk_pop
 )
 
 # transpose contact matrix

episodes/simulating-transmission.Rmd

@@ -65,6 +65,7 @@ In this tutorial we are going to learn how to use the `{epidemics}` package to s
 library(epidemics)
 library(contactsurveys)
 library(socialmixr)
+library(wpp2024)
 library(tidyverse)
 ```
 
@@ -183,13 +184,20 @@ survey_files <- contactsurveys::download_survey(
 )
 survey_load <- socialmixr::load_survey(files = survey_files)
 
+data(popAge1dt, package = "wpp2024")
+
+uk_pop <- popAge1dt %>%
+  dplyr::filter(name == "United Kingdom", year == max(year)) %>%
+  dplyr::select(lower.age.limit = age, population = pop) %>%
+  dplyr::mutate(population = population * 1000)
+
 # Generate the contact matrix
 contacts_byage <- socialmixr::contact_matrix(
   survey = survey_load,
   countries = "United Kingdom",
   age_limits = c(0, 20, 40),
   symmetric = TRUE,
-  return_demography = TRUE
+  survey_pop = uk_pop
 )
 
 # prepare contact matrix
@@ -301,6 +309,18 @@ uk_population <- epidemics::population(
 )
 ```
 
+:::::::::::: checklist
+
+Print the `uk_population` object. 
+
+It must collect all the input information:
+
+- Population name
+- Demography
+- Contact matrix
+- Initial conditions
+
+::::::::::::
 
 :::::::::::::::::::::: instructor
 

episodes/vaccine-comparisons.Rmd

@@ -42,6 +42,7 @@ library(ggplot2)
 library(epidemics)
 library(contactsurveys)
 library(socialmixr)
+library(wpp2024)
 library(dplyr)
 library(purrr)
 ```
@@ -74,12 +75,19 @@ survey_files <- contactsurveys::download_survey(
 )
 survey_load <- socialmixr::load_survey(files = survey_files)
 
+data(popAge1dt, package = "wpp2024")
+
+uk_pop <- popAge1dt %>%
+  dplyr::filter(name == "United Kingdom", year == max(year)) %>%
+  dplyr::select(lower.age.limit = age, population = pop) %>%
+  dplyr::mutate(population = population * 1000)
+
 contacts_byage <- socialmixr::contact_matrix(
   survey = survey_load,
   countries = "United Kingdom",
   age_limits = c(0, 15, 65),
   symmetric = TRUE,
-  return_demography = TRUE
+  survey_pop = uk_pop
 )
 
 

learners/files/baseline-interventions-en.R

@@ -3,17 +3,30 @@
 # load packages
 library(epidemics)
 library(socialmixr)
+library(contactsurveys)
 library(tidyverse)
 
 # load survey data
-survey_data <- socialmixr::polymod
+survey_files <- contactsurveys::download_survey(
+  survey = "https://doi.org/10.5281/zenodo.3874557",
+  verbose = FALSE
+)
+survey_load <- socialmixr::load_survey(files = survey_files)
+
+data(popAge1dt, package = "wpp2024")
+
+uk_pop <- popAge1dt %>%
+  dplyr::filter(name == "United Kingdom", year == max(year)) %>%
+  dplyr::select(lower.age.limit = age, population = pop) %>%
+  dplyr::mutate(population = population * 1000)
 
 # generate contact matrix
 cm_results <- socialmixr::contact_matrix(
-  survey = survey_data,
+  survey = survey_load,
   countries = "United Kingdom",
-  age.limits = c(0, 15, 65),
-  symmetric = TRUE
+  age_limits = c(0, 15, 65),
+  symmetric = TRUE,
+  survey_pop = uk_pop
 )
 
 # transpose contact matrix

learners/setup.md

@@ -13,7 +13,7 @@
 Our tutorials are built around an outbreak analysis pipeline split into three stages: **Early tasks**, **Middle tasks** and **Late tasks**. The outputs of tasks completed in earlier stages commonly feed into the tasks required for later ones.
 
 
 ![An overview of the tutorial topics arranged by the three stages of outbreak analytics.](https://epiverse-trace.github.io/task_pipeline-minimal.svg)
 
 Each stage has its tutorial website and each tutorial website consists of a set of episodes covering different topics.
 
@@ -36,7 +36,7 @@
 
 Our strategy is to gradually incorporate specialised **R packages** into a traditional analysis pipeline. These packages should fill the gaps in these epidemiology-specific tasks in response to outbreaks.
 
 ![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))](episodes/fig/pkgs-hexlogos-2.png).
 
 :::::::::::::::::::::::::::: prereq
 
@@ -173,6 +173,8 @@
 
 new_packages <- c(
   "socialmixr",
+  "contactsurveys",
+  "PPgp/wpp2024",
   "finalsize",
   "epiverse-trace/epidemics",
   "epiparameter",
@@ -247,6 +249,8 @@
 
 ```r
 library(socialmixr)
+library(contactsurveys)
+library(wpp2024)
 library(finalsize)
 library(epidemics)
 library(epiparameter)

renv/profiles/lesson-requirements/renv.lock

@@ -5354,6 +5354,32 @@
       "NeedsCompilation": "no",
       "Repository": "CRAN"
     },
+    "wpp2024": {
+      "Package": "wpp2024",
+      "Version": "1.1-3",
+      "Source": "GitHub",
+      "Date": "2024-11-26",
+      "Title": "World Population Prospects 2024",
+      "Author": "United Nations Population Division",
+      "Maintainer": "Hana Sevcikova <hanas@uw.edu>",
+      "Depends": [
+        "R (>= 2.14.2)",
+        "data.table"
+      ],
+      "Suggests": [],
+      "Description": "Provides data from the United Nation's World Population Prospects 2024.",
+      "License": "file LICENSE",
+      "URL": "http://population.un.org/wpp",
+      "NeedsCompilation": "no",
+      "LazyData": "no",
+      "RemoteType": "github",
+      "RemoteHost": "api.github.com",
+      "RemoteRepo": "wpp2024",
+      "RemoteUsername": "PPgp",
+      "RemotePkgRef": "PPgp/wpp2024",
+      "RemoteRef": "HEAD",
+      "RemoteSha": "2da7768ae64fc74105d3f9e98f9a74d37b62f99a"
+    },
     "xfun": {
       "Package": "xfun",
       "Version": "0.58",