Mathematical Modeling of Infectious Disease
EpiModel is an R package that provides tools for simulating and analyzing mathematical models of infectious disease. Supported epidemic model classes include deterministic compartmental models, stochastic individual contact models, and stochastic network models. Disease types include SI, SIR, and SIS epidemics with and without demography, with utilities available for expansion to construct and simulate epidemic models of arbitrary complexity. The network model class is based on the statistical framework of temporal exponential random graph models (ERGMs) implementated in the Statnet suite of software for R.
The current software version is EpiModel v1.2.8, which may be downloaded from CRAN and installed in R through:
The development version of EpiModel is hosted on GitHub and may be installed via the devtools package by:
install.packages("EpiModel", dependencies = TRUE)
The software source code is available at the Github Repository. Users should submit bug reports and feature requests as issues there. The Releases page on the repository lists all the changes to the software over time.
The EpiModel Software Manual provides a list of all the main functions within the package, with syntax and examples. This documentation is also available within the package by consulting the help files.
A good place to start learning about EpiModel is the main vignette, currently under review, but available in pre-press form here!
For beginning EpiModel users and those new to mathematical modeling
generally, EpiModel includes two web-based applications for simulating
epidemics, using the Shiny
framework in R. These applications are included within EpiModel
for deterministic compartmental models (DCMs), stochastic individual
contact models (ICMs), and stochastic network models. They are also hosted
online at the links below.
DCMs ICMs Network
The Tutorials page provides introductions to running epidemic models of the three classes supported in EpiModel, and then expanding those models to address novel research questions. For greater theoretical background to fitting stochastic network models specifically, consult the Workshops page to view the materials from our in-person courses on using EpiModel.