knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)

Assessing the performance of real-time epidemic forecasts: A case study of Ebola in the Western Area region of Sierra Leone, 2014-15

This repository contains the data and code for our paper:

Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, Rosalind M. Eggo and W. John Edmunds (2019). Assessing the performance of real-time epidemic forecasts: A case study of Ebola in the Western Area region of Sierra Leone, 2014-15. PLoS Comput Biol 15(2): e1006785. https://doi.org/10.1371/journal.pcbi.1006785

How to download or install

You can install the code and data as an R package, ebola.forecast.wa.sl, from GitHub with:

devtools::install_github("sbfnk/ebola.forecast.wa.sl")

Included data sets

The package includes five data sets. One of them contains the data of Ebola cases from Western Area that was used for the analysis. It can be loaded with

data(ebola_wa)

The other four data sets contain Monte-Carlo samples from the semi-mechanistic model used for forecasts, as well as the three null models. The data sets are called samples_semi_mechanistic, samples_bsts, samples_deterministic and samples_deterministic, and they can be loaded with

data(samples)

The data sets from the null models can be re-created using

samples_bsts <- null_model_bsts()
samples_deterministic <- null_model_deterministic()
samples_unfocused <- null_model_unfocused()

Table and figures

The table and figures in the manuscript can be re-created using

t1 <- table1()
print(t1)
t2 <- table2()
print(t2)

library('cowplot')
p1 <- figure1()
p2 <- figure2()
p3 <- figure3()
p4 <- figure4()
ggsave("figure1.pdf", p1, width=18, height=6.6, dpi=300, units="cm")
ggsave("figure2.pdf", p2, width=18, height=6.6, dpi=300, units="cm")
ggsave("figure3.pdf", p3, width=18, height=11.1, dpi=300, units="cm")
ggsave("figure4.pdf", p4, width=15.9, height=11.1, dpi=300, units="cm")


sbfnk/ebola.forecast.wa.sl documentation built on Feb. 18, 2020, 6:19 p.m.