knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "README-" ) # Please put your title here to include it in the file below. Title <- "The impact of reactive mass vaccination campaigns on measles outbreaks in the Katanga region, Democratic Republic of Congo"
This repository contains the data and code for our paper:
Sebastian Funk, Saki Takahashi, Joel Hellewell, Kartini Gadroen, Isidro Carrion-Martin, Marit van Lenthe, Katiana Rivette, Sebastian Dietrich, W. John Edmunds, M. Ruby Siddiqui and V. Bhargavi Rao (2019).
r Title
. medRxiv 19003434 https://doi.org/10.1101/19003434
You can download the compendium as a zip from from this URL: https://github.com/sbfnk/measles.katanga/archive/master.zip
Or you can install this compendium as an R package, measles.katanga, from GitHub with:
# install.packages("remotes") remotes::install_github("sbfnk/measles.katanga")
Figures 1-4 can be re-created using
figure1() figure2() figure3() figure4()
Figure 5 uses the outputs from the prediction model, which can be run using
p <- prediction_model() figure5(p)
Figure 6 uses the outputs from the dynamic model. These are included in the model as libbi
objects which can be retrieved using.
posterior <- rbi::read_libbi(system.file(package="measles.katanga", file.path("bi", "posterior.rds"))) posterior_no_mvc <- rbi::read_libbi(system.file(package="measles.katanga", file.path("bi", "posterior_no_mvc.rds")))
The two variables posterior
and posterior_no_mvc
then contain results from posterior sampling and using these to resimulate without mass vaccination campaigns, respectively. These can be re-created using
model <- posterior$model posterior <- fit_dynamic_model(model, nbdata=10) posterior_no_mvc <- remove_mvc(posterior)
The fit_dynamic_model
command may take a few hours to run, depending on the hardware available (especially subject to availability of a fast Graphical Processing Unit, GPU).
From these two variables, Figure 6 can be re-created using
figure6(posterior, posterior_no_mvc)
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