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>. The package also provides functionality to fit models to time series data using the Approximate Bayesian Computation Sequential Monte Carlo ('ABC-SMC') algorithm of Toni and others (2009) <doi:10.1098/rsif.2008.0172>.

Package details

AuthorStefan Widgren [aut, cre] (<>), Robin Eriksson [aut] (<>), Stefan Engblom [aut] (<>), Pavol Bauer [aut] (<>), Thomas Rosendal [ctb] (<>), Ivana Rodriguez Ewerlöf [ctb] (<>), Attractive Chaos [cph] (Author of 'kvec.h'.)
MaintainerStefan Widgren <>
Package repositoryView on CRAN
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SimInf documentation built on Sept. 4, 2022, 1:06 a.m.