SimInf: An R package for Data-driven Stochastic Disease Spread Simulations

Abstract

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 OpenMP to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goal 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.

The manual has been updated to the latest version of SimInf (v6.0.0). 41 pages, 16 figures

References in corpus (2)

• Dynamical Patterns of Cattle Trade Movements
• Avoidable errors in the modeling of outbreaks of emerging pathogens, with special reference to Ebola

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