This package compiles a series of publicly available disease outbreak data. Data can be provided as R objects (loaded automatically when loading the package), text files distributed alongside the package, or functions generating a dataset.

The following R datasets are currently available:

data(package="outbreaks")
Data sets in outbreaks
Item Title
dengue_fais_2011 Dengue on the island of Fais, Micronesia, 2011
dengue_yap_2011 Dengue on the Yap Main Islands, Micronesia, 2011
ebola_kikwit_1995 Ebola in Kikwit, Democratic Republic of the Congo, 1995
ebola_sim Simulated Ebola outbreak
ebola_sim_clean Simulated Ebola outbreak
fluH7N9_china_2013 Influenza A H7N9 in China, 2013
influenza_england_1978_school Influenza in a boarding school in England, 1978
measles_hagelloch_1861 Measles in Hagelloch, Germany, 1861
mers_korea_2015 Middle East respiratory syndrome in South Korea, 2015
norovirus_derbyshire_2001_school Norovirus in a primary school in Derbyshire, England, 2001
s_enteritidis_pt59 Salmonella Enteritidis PT59 outbreak
sars_canada_2003 Severe Acute Respiratory Syndrome in Canada, 2003
smallpox_abakaliki_1967 Smallpox in Abakaliki, Nigeria, 1967
zika_girardot_2015 Zika in Girardot, Colombia, 2015
zika_sanandres_2015 Zika in San Andres, Colombia, 2015
zika_yap_2007 Zika on the Yap Main Islands, Micronesia, 2007


Installing the package

To install the current stable, CRAN version of the package, type:

install.packages("outbreaks")

To benefit from the latest features and bug fixes, install the development, github version of the package using:

devtools::install_github("reconhub/outbreaks")

Note that this requires the package devtools installed.


Add your own data!

How to add data?

We will try to create a better repository and data submission system at a later stage. The purpose of the current package is only to share examplar datasets during the hackathon. Acceptable forms are: - as a .RData files in the data/ folder (recommended) - as a text file in the inst/ folder - as a function loading/assembling/simulating a dataset

Naming Conventions

We use the lower case throughout, and snake_case (using underscores) to separate words for the files and dataset names, so that for a RData object, a new dataset woud look like: `my_new_data_RData’. Try using informative names, typically using the disease first. Whenever available, order fields as: 1. disease: mandatory 2. location: optional 3. year: optional 4. sim: mandatory if this is a simulated dataset; otherwise data is assume to be an actual outbreak 5. other: (any other relevant information)


Contributors (by alphabetic order):

Maintainer: Finlay Campbell (f.campbell15@imperial.ac.uk)