README.md

measles.katanga

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

How to download or install

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 and analysis

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)


sbfnk/measles.katanga documentation built on Nov. 11, 2019, 4:27 a.m.