Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Stefan Widgren, Pavol Bauer, Robin Eriksson, Stefan Engblom
Title: SimInf: An R Package for Data-Driven Stochastic Disease Spread Simulations
Abstract: We present the R package SimInf which provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in subpopulations as continuous-time Markov chains using the Gillespie stochastic simulation algorithm and incorporates available data such as births, deaths and movements as scheduled events at predefined time-points. Using C code for the numerical solvers and divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goals was to make SimInf extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. In this paper, we provide a technical description of the framework and demonstrate its use on some basic examples. We also discuss how to specify and extend the framework with user-defined models.

Page views:: 917. Submitted: 2017-06-22. Published: 2019-11-12.
Paper: SimInf: An R Package for Data-Driven Stochastic Disease Spread Simulations     Download PDF (Downloads: 146)
Supplements:
SimInf_6.4.0.tar.gz: R source package Download (Downloads: 4; 2MB)
v91i12.R: R replication code Download (Downloads: 9; 23KB)

DOI: 10.18637/jss.v091.i12

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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.