Introduction

The EpiModel package provides tools for simulating mathematical models of infectious disease dynamics. Epidemic model classes include deterministic compartmental models, stochastic agent-based models, and stochastic network models. Network models use the robust statistical methods of temporal exponential-family random graph models (ERGMs) from the Statnet suite of software packages in R. Standard templates for epidemic modeling include SI, SIR, and SIS disease types. EpiModel features an API for extending these templates to address novel scientific research aims.

Documentation

This vignette is a placeholder for the EpiModel tutorials and documentation hosted online and external to the package to minimize testing and building timing. Tutorials may be found at the EpiModel website (http://www.epimodel.org).

A good place to start learning about EpiModel is the main methods paper. It is published Journal of Statistical Software at the reference below.

Within the package, please consult the extensive help documentation:

help(package = "EpiModel")

To see the latest updates to EpiModel, consult the software NEWS file on Github (https://github.com/statnet/EpiModel/releases).

Getting Help

We have historically used both an email-based listserv and Github issues to address questions, bug reports, and feature requests for our users. With the release of EpiModel v2.0.3, we have decided to close the email listserv, and provide this user support entirely with Github issues. Any technical coding and non-technical conceptual EpiModel questions or requests may be posted as a Github issue at our main Github repository (https://github.com/statnet/EpiModel/issues).

Citation

If using EpiModel for teaching or research, please include a citation:

Jenness SM, Goodreau SM and Morris M. EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks. Journal of Statistical Software. 2018; 84(8): 1-47. doi: 10.18637/jss.v084.i08 (https://doi.org/10.18637/jss.v084.i08).