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. The use of the package is demonstrated through examples involving both simulated and real data.
Highlights
The task of modelling infectious disease transmission through a population poses a number of challenges
Deardon et al (2010) introduced a class of discrete time individuallevel models (ILMs), fitting the models to data in a Bayesian Markov chain Monte Carlo (MCMC) framework. They applied spatial ILMs to the UK foot-and-mouth disease (FMD) epidemic of 2001, which accounted for farm-level covariates such as the number and type of animals on each farm
This paper discusses the implementation of the R software package EpiILM
Summary
The task of modelling infectious disease transmission through a population poses a number of challenges. The output of the function epidata() is formed as class of epidata object This epidata object contains a list that consist of type (the compartmental framework), XYcoordinates(the XY coordinates of individual for spatial model) or contact (the contact network matrix for the network model), inftime (the infection times) and remtime (the removal times). Other functions such as plot.epidata and epimcmc involved in the package use this object class as an input argument. Empirical mean and standard deviation for each variable, plus standard error of the mean: Mean
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