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.

Version: 6.2.0
Depends: R (≥ 3.1)
Imports: graphics, grDevices, methods, stats, utils, Matrix
Published: 2018-11-20
Author: Stefan Widgren ORCID iD [aut, cre], Robin Eriksson ORCID iD [aut], Stefan Engblom ORCID iD [aut], Pavol Bauer ORCID iD [aut], Attractive Chaos [cph] (Author of 'kvec.h', a generic dynamic array)
Maintainer: Stefan Widgren <stefan.widgren at>
License: GPL-3
NeedsCompilation: yes
SystemRequirements: GNU Scientific Library (GSL)
Citation: SimInf citation info
Materials: NEWS
CRAN checks: SimInf results


Reference manual: SimInf.pdf
Vignettes: SimInf: An R Package for Data-Driven Stochastic Disease Spread Simulations
Package source: SimInf_6.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: SimInf_6.2.0.tgz, r-oldrel: SimInf_6.2.0.tgz
Old sources: SimInf archive


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