stewid/SimInf: A Framework for Data-Driven Stochastic Disease Spread Simulations

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' (if available) to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goals was to make the package extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. The package contains template models and can be extended with user-defined models. For more details see the paper by Widgren, Bauer, Eriksson and Engblom (2019) <doi:10.18637/jss.v091.i12>.

Getting started

Package details

Maintainer
LicenseGPL-3
Version6.4.0.9000
URL https://github.com/stewid/SimInf
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("stewid/SimInf")
stewid/SimInf documentation built on Feb. 6, 2020, 12:14 a.m.