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