文章基本信息

标题：SimInf: An R Package for Data-Driven Stochastic Disease Spread Simulations
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作者：Stefan Widgren ; Pavol Bauer ; Robin Eriksson 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2019
卷号：91
期号：1
页码：1-42
DOI：10.18637/jss.v091.i12
出版社：University of California, Los Angeles
摘要：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.
关键词：computational epidemiology; discrete-event simulation; multicore implementation;
其他关键词：computational epidemiology;discrete-event simulation;multicore implementation;stochastic modeling