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). The impact of reactive mass vaccination campaigns on measles outbreaks in the Katanga region, Democratic Republic of Congo. 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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