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

TL;DR

SimInf is an R package that enables efficient, flexible, and scalable data-driven stochastic epidemiological modeling using continuous-time Markov chains and parallel computing.

Contribution

The paper introduces SimInf, a novel R package that combines stochastic simulation, data integration, and extendability for large-scale disease spread modeling.

Findings

High performance through C and OpenMP implementation

Flexible framework for data-driven epidemiological modeling

Extendable design for custom models

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.