@article{JSSv091i12, title={SimInf: An R Package for Data-Driven Stochastic Disease Spread Simulations}, volume={91}, url={https://www.jstatsoft.org/index.php/jss/article/view/v091i12}, doi={10.18637/jss.v091.i12}, 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 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.}, number={12}, journal={Journal of Statistical Software}, author={Widgren, Stefan and Bauer, Pavol and Eriksson, Robin and Engblom, Stefan}, year={2019}, pages={1–42} }