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.

Data-Driven Network Modeling as a Framework to Evaluate the Transmission of Piscine Myocarditis Virus (PMCV) in the Irish Farmed Atlantic Salmon Population and the Impact of Different Mitigation Measures

“…The disease spread model was implemented in SimInf (12,38), which is an R (39) package for data-driven stochastic disease spread simulations. This package was adapted, in part, from the Unstructured Mesh Reaction-Diffusion Master Equation (URDME) framework (40,41).…”

“…It interfaces high performance compiled code (including a core algorithm written in C) (42) and OpenMP, which allows work to be divided across multiple processors and computations to be performed in parallel. Implementation and data structures of the simulation algorithm are presented elsewhere (11,12). The disease spread simulations were performed using the SimInf package version 5.1.0 (model SISe3_sp) and R version 3.4.2.…”

“…However, there are considerable computational challenges when stochastic simulations are conducted using livestock data, both computationally, including the need for efficient algorithms, and also with model selection and parameter inference ( 10 ). An efficient modeling framework for event-based epidemiological simulations of infectious diseases has recently been developed ( 11 , 12 ), including the use of a framework that integrates within-farm infection dynamics as continuous-time Markov chains and livestock data as scheduled events. This approach was recently used to model the spread of Verotoxigenic Escherichia coli O157:H7 (VTEC O157) in Swedish cattle ( 13 , 14 ).…”

A between-herd data-driven stochastic model to explore the spatio-temporal spread of hepatitis E virus in the French pig production network

“…Several approaches have been recently used to implement such models that may be computationally challenging [ 32 – 34 ]. In particular, the SimInf package developed in R software is recognized as an efficient and flexible modelling framework for fast event-based epidemiological simulations of infectious disease spread [ 32 ]. It makes it possible to integrate within-herd infection dynamics as a continuous-time Markov process and demographic data as scheduled events.…”

EMULSION: transparent and flexible multiscale stochastic models in epidemiology

“…Simulation libraries and platforms dedicated to epidemiological issues are rising, e.g. SimInf [9], a R library for data-driven compartment-based models; MicroSim [10], an agent-based platform for several kinds of diseases; or GLEaMviz [11], a metapopulation-oriented platform. To our knowledge, the most advanced approach in terms of diversity of modelling paradigms is Broadwick [12], a Java framework for compartment-and individual-based models with interaction networks, which nevertheless still requires writing large portions of code to derive specific classes and carry out simulations on practical cases.…”