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SimInf: An R Package for Data-Driven Stochastic Disease Spread Simulations
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Natl Vet Inst, Dept Dis Control & Epidemiol, SE-75189 Uppsala, Sweden.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.ORCID iD: 0000-0002-3614-1732
2019 (English)In: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 91, no 12, p. 1-42Article in journal (Refereed) Published
Abstract [en]

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 Simlnf 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.

Place, publisher, year, edition, pages
2019. Vol. 91, no 12, p. 1-42
Keywords [en]
computational epidemiology, discrete-event simulation, multicore implementation, stochastic modeling
National Category
Computational Mathematics Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-397957DOI: 10.18637/jss.v091.i12ISI: 000495940100001OAI: oai:DiVA.org:uu-397957DiVA, id: diva2:1382218
Funder
Swedish Research CouncilSwedish Research Council FormasAvailable from: 2020-01-02 Created: 2020-01-02 Last updated: 2020-01-03Bibliographically approved

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Widgren, StefanBauer, PavolEriksson, RobinEngblom, Stefan
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