differences for PR #128

Author: actions-user

.DS_Store

@@ -0,0 +1,86 @@
+#------------------------------------------------------------
+# Values for this lesson.
+#------------------------------------------------------------
+
+# Which carpentry is this (swc, dc, lc, or cp)?
+# swc: Software Carpentry
+# dc: Data Carpentry
+# lc: Library Carpentry
+# cp: Carpentries (to use for instructor training for instance)
+# incubator: The Carpentries Incubator
+carpentry: 'epiverse-trace'
+
+# Overall title for pages.
+title: 'Scenario modelling for outbreak analytics with R'
+
+# Date the lesson was created (YYYY-MM-DD, this is empty by default)
+created: 
+
+# Comma-separated list of keywords for the lesson
+keywords: 'epidemic models, interventions'
+
+# Life cycle stage of the lesson
+# possible values: pre-alpha, alpha, beta, stable
+life_cycle: 'pre-alpha'
+
+# 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-late'
+
+# Default branch of your lesson
+branch: 'main'
+
+# Who to contact if there are any issues
+contact: 'andree.valle-campos@lshtm.ac.uk'
+
+# Navigation ------------------------------------------------
+#
+# Use the following menu items to specify the order of
+# individual pages in each dropdown section. Leave blank to
+# include all pages in the folder.
+#
+# Example -------------
+#
+# episodes:
+# - introduction.md
+# - first-steps.md
+#
+# learners:
+# - setup.md
+#
+# instructors:
+# - instructor-notes.md
+#
+# profiles:
+# - one-learner.md
+# - another-learner.md
+
+# Order of episodes in your lesson
+episodes:
+- contact-matrices.Rmd
+- simulating-transmission.Rmd
+- model-choices.Rmd
+- modelling-interventions.Rmd
+- compare-interventions.Rmd
+- vaccine-comparisons.Rmd
+- disease-burden.Rmd
+
+# Information for Learners
+learners: 
+
+# Information for Instructors
+instructors: 
+
+# Learner 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
+sandpaper: epiverse-trace/sandpaper@patch-renv-github-bug

config.yaml

@@ -62,6 +62,7 @@ $$
 1 & 3 
 \end{bmatrix}
 $$
+
 In this example, we would use this to represent that children meet, on average, 2 other children and 2 adult per day (first row), and adults meet, on average, 1 child and 3 other adults per day (second row). We can use this kind of information to account for the role heterogeneity in contact plays in infectious disease transmission.
 
 ::::::::::::::::::::::::::::::::::::: callout
@@ -314,6 +315,7 @@ $$
 \frac{dR}{dt} &=\gamma I \\
 \end{aligned}
 $$
+
 To add age structure to our model, we need to add additional equations for the infection states $S$, $I$ and $R$ for each age group $i$. If we want to assume that there is heterogeneity in contacts between age groups then we must adapt the transmission term $\beta SI$ to include the contact matrix $C$ as follows :
 
 $$ \beta S_i \sum_j C_{i,j} I_j/N_j. $$ 

contact-matrices.md

@@ -4,10 +4,10 @@
 "config.yaml" "a0c04c1d43ce0640c3ea333b140e89c8" "site/built/config.yaml" "2025-11-22"
 "index.md" "32bc80d6f4816435cc0e01540cb2a513" "site/built/index.md" "2025-11-22"
 "links.md" "8184cf4149eafbf03ce8da8ff0778c14" "site/built/links.md" "2025-11-22"
-"episodes/contact-matrices.Rmd" "19856620d33f9b7f4e8ee312460494f1" "site/built/contact-matrices.md" "2025-11-22"
+"episodes/contact-matrices.Rmd" "9e9d596cf25f68a50522b15a234472ac" "site/built/contact-matrices.md" "2025-11-22"
 "episodes/simulating-transmission.Rmd" "3e3ecf82148896a33189045d704f9606" "site/built/simulating-transmission.md" "2025-11-22"
 "episodes/model-choices.Rmd" "aa195e66455fb6a97b4930fd08c08001" "site/built/model-choices.md" "2025-11-22"
-"episodes/modelling-interventions.Rmd" "79b561f3ee274407dddbda6a1b9c1a68" "site/built/modelling-interventions.md" "2025-11-22"
+"episodes/modelling-interventions.Rmd" "c70c9baddc80d94eebd2e50f7efc72cb" "site/built/modelling-interventions.md" "2025-11-22"
 "episodes/compare-interventions.Rmd" "2ef6697bbad9bcfb843ab9d50469123b" "site/built/compare-interventions.md" "2025-11-22"
 "episodes/vaccine-comparisons.Rmd" "bb9110b17b2b5cdc915df3f17eae15df" "site/built/vaccine-comparisons.md" "2025-11-22"
 "episodes/disease-burden.Rmd" "9deead362349c98be8f1d9380a7b975a" "site/built/disease-burden.md" "2025-11-22"

md5sum.txt

@@ -50,7 +50,7 @@ In this tutorial different types of intervention and how they can be modelled ar
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
 
-## A baseline model
+## Baseline model
 
 We will investigate the effect of interventions on a COVID-19 outbreak using an SEIR model (`model_default()` in the R package `{epidemics}`). To be able to see the effect of our intervention, we will run a baseline variant of the model, i.e,  without intervention.
 
@@ -146,7 +146,7 @@ rownames(cm_matrix)
 [1] "[0,15)"  "[15,65)" "65+"    
 ```
 
-Therefore, we specify ` reduction = matrix(c(0.5, 0.01, 0.01))`. We assume that the school closures start on day 50 and continue to be in place for a further 100 days. Therefore our intervention object is: 
+Therefore, we specify `reduction = matrix(c(0.5, 0.01, 0.01))`. We assume that the school closures start on day 50 and continue to be in place for a further 100 days. Therefore our intervention object is: 
 
 
 ``` r
@@ -255,7 +255,7 @@ We can also model the effect of other NPIs by reducing the value of the relevant
 
 We expect that mask wearing will reduce an individual's infectiousness, based on multiple studies showing the effectiveness of masks in reducing transmission. As we are using a population-based model, we cannot make changes to individual behavior and so assume that the transmission rate $\beta$ is reduced by a proportion due to mask wearing in the population. We specify this proportion, $\theta$ as product of the proportion wearing masks multiplied by the proportion reduction in transmission rate (adapted from [Li et al. 2020](https://doi.org/10.1371/journal.pone.0237691)).
 
-We create an intervention object with `type = rate` and `reduction = 0.161`. Using parameters adapted from [Li et al. 2020](https://doi.org/10.1371/journal.pone.0237691) we have proportion wearing masks = coverage $\times$ availability = $0.54 \times 0.525 = 0.2835$ and proportion reduction in transmission rate = $0.575$. Therefore, $\theta = 0.2835 \times 0.575 = 0.163$. We assume that the mask wearing mandate starts at day 40 and continue to be in place for 200 days.
+We create an intervention object with `type = "rate"` and `reduction = 0.161`. Using parameters adapted from [Li et al. 2020](https://doi.org/10.1371/journal.pone.0237691) we have proportion wearing masks = coverage $\times$ availability = $0.54 \times 0.525 = 0.2835$ and proportion reduction in transmission rate = $0.575$. Therefore, $\theta = 0.2835 \times 0.575 = 0.163$. We assume that the mask wearing mandate starts at day 40 and continue to be in place for 200 days.
 
 
 ``` r
@@ -358,6 +358,7 @@ $$
 \frac{dV_i}{dt} & =\nu_{i,t} S_i\\
 \end{aligned}
 $$
+
 Individuals in age group ($i$)  at specific time dependent ($t$)  are vaccinated at rate ($\nu_{i,t}$). The other SEIR components of these equations are described in the tutorial [simulating transmission](../episodes/simulating-transmission.md#simulating-disease-spread). 
 
 To explore the effect of vaccination we need to create a vaccination object to pass as an input into `model_default()` that includes  age groups specific vaccination rate `nu` and age groups specific start and end times of the vaccination program (`time_begin` and `time_end`).