The R Journal: article published in 2020, volume 12:1

Individual-Level Modelling of Infectious Disease Data: EpiILM
Vineetha Warriyar K. V., Waleed Almutiry and Rob Deardon , The R Journal (2020) 12:1, pages 87-104.

Abstract 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.

Received: 2019-03-04; online 2020-09-10
CRAN packages: R0, EpiEstim, EpiModel, epinet, surveillance, EpiILM, igraph, ergm, adaptMCMC, coda
CRAN Task Views implied by cited CRAN packages: gR, Bayesian, Environmetrics, Graphics, Optimization, SocialSciences, Spatial, SpatioTemporal, TimeSeries

This article is licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2020-020,
  author = {Vineetha Warriyar K. V. and Waleed Almutiry and Rob Deardon},
  title = {{Individual-Level Modelling of Infectious Disease Data:
          EpiILM}},
  year = {2020},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2020-020},
  url = {https://doi.org/10.32614/RJ-2020-020},
  pages = {87--104},
  volume = {12},
  number = {1}
}