SimInf: A Framework for Data-Driven Stochastic Disease Spread Simulations

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 'OpenMP' (if available) to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goals was to make the package extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. The package contains template models and can be extended with user-defined models. For more details see the paper by Widgren, Bauer, Eriksson and Engblom (2019) <doi:10.18637/jss.v091.i12>.

Version:	7.0.1
Depends:	R (≥ 3.3)
Imports:	digest, graphics, grDevices, methods, stats, utils, Matrix
Published:	2020-06-18
Author:	Stefan Widgren [aut, cre], Robin Eriksson [aut], Stefan Engblom [aut], Pavol Bauer [aut], Thomas Rosendal [ctb], Attractive Chaos [cph] (Author of 'kvec.h'.)
Maintainer:	Stefan Widgren <stefan.widgren at gmail.com>
BugReports:	https://github.com/stewid/SimInf/issues
License:	GPL-3
URL:	https://github.com/stewid/SimInf
NeedsCompilation:	yes
SystemRequirements:	GNU Scientific Library (GSL)
Citation:	SimInf citation info
Materials:	README NEWS
CRAN checks:	SimInf results

Downloads:

Linking:

Please use the canonical form https://CRAN.R-project.org/package=SimInf to link to this page.