In this article we introduce the R package EpiILM, which provides tools for simulation from, and inference for, discrete-time individual-level models of infectious disease transmission proposed by Deardon et al. (2010). The inference is set in a Bayesian framework and is carried out via Metropolis Hastings Markov chain Monte Carlo (MCMC). For its fast implementation, key functions are coded in Fortran. Both spatial and contact network models are implemented in the package and can be set in either susceptible-infected (SI) or susceptible-infected-removed (SIR) compartmental frameworks. Use of the package is demonstrated through examples involving both simulated and real data.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2020-020.zip
R0, EpiEstim, EpiModel, epinet, surveillance, EpiILM, igraph, ergm, adaptMCMC, coda
gR, Bayesian, Environmetrics, Graphics, Optimization, SocialSciences, Spatial, SpatioTemporal, TimeSeries
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For attribution, please cite this work as
V., et al., " Individual-Level Modelling of Infectious Disease Data: EpiILM", The R Journal, 2020
BibTeX citation
@article{RJ-2020-020,
author = {V., Vineetha Warriyar K. and Almutiry, Waleed and Deardon, Rob},
title = { Individual-Level Modelling of Infectious Disease Data: EpiILM},
journal = {The R Journal},
year = {2020},
note = {https://doi.org/10.32614/RJ-2020-020},
doi = {10.32614/RJ-2020-020},
volume = {12},
issue = {1},
issn = {2073-4859},
pages = {87-104}
}