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README.md

IDSpatialStats

This GitHub repository provides source code for the IDSpatialStats R package, which is designed to help epidemiologists assess the scale of spatial and temporal dependence in epidemic case occurrence data.

The current implementation of the package includes a function which simulates infectious disease spread as a spatial branching process, along with two novel spatial statistics that estimate: 1) the mean of the spatial transmission kernel, which is a measure of fine-scale spatial dependence between two cases, and 2) the tau-statistic, a measure of global clustering based on pathogen subtype.

Detailed description of the methods can be found here:

The IDSpatialStats R package: Quantifying spatial dependence of infectious disease spread (Giles et al. 2019)

Measuring spatial dependence for infectious disease epidemiology (Lessler et al. 2016)

Estimating infectious disease transmission distances using the overall distribution of cases (Salje et al. 2016)

This package is maintained by John Giles (@gilesjohnr) and Justin Lessler (@jlessler) as part of the Johns Hopkins Bloomberg School of Public Health Infectious Disease Dynamics team (@HopkinsIDD).

Installation

To install the offical release of the IDSpatialStats package, open R and type:

install.packages('IDSpatialStats')

To install the install the development version, first install the devtools package and then install IDSpatialStats from source via GitHub:

install.packages('devtools')
devtools::install_github('HopkinsIDD/IDSpatialStats')

Troubleshooting

For general questions, contact package maintainers John Giles (giles@jhu.edu) or Justin Lessler (justin@jhu.edu).

To report bugs or problems with documentation, please go to the Issues page associated with this GitHub page and click new issue.

If you wish to contribute to IDSpatialStats, please get in touch via email and then fork the latest version of the package. After committing your code to your own forked version, submit a pull request when you are ready to share.

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