Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Waleed Almutiry, Vineetha Warriyar K V, Rob Deardon
Title: Continuous Time Individual-Level Models of Infectious Disease: Package EpiILMCT
Abstract: This paper describes the R package EpiILMCT, which allows users to study the spread of infectious disease using continuous time individual level models (ILMs). The package provides tools for simulation from continuous time ILMs that are based on either spatial demographic, contact network, or a combination of both of them, and for the graphical summarization of epidemics. Model fitting is carried out within a Bayesian Markov Chain Monte Carlo framework. The continuous time ILMs can be implemented within either susceptible-infected-removed (SIR) or susceptible-infected-notified-removed (SIN R) compartmental frameworks. As infectious disease data is often partially observed, data uncertainties in the form of missing infection times - and in some situations missing removal times - are accounted for using data augmentation techniques. The package is illustrated using both simulated and an experimental data set on the spread of the tomato spotted wilt virus disease.

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Paper: Continuous Time Individual-Level Models of Infectious Disease: Package EpiILMCT     Download PDF (Downloads: 106)
EpiILMCT_1.1.7.tar.gz: R source package Download (Downloads: 3; 241KB)
v98i10.R: R replication code Download (Downloads: 14; 17KB)

DOI: 10.18637/jss.v098.i10

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.