SimInf (6.4.0)

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) .

Maintainer: Stefan Widgren
Author(s): Stefan Widgren [aut, cre] (<>), Robin Eriksson [aut] (<>), Stefan Engblom [aut] (<>), Pavol Bauer [aut] (<>), Attractive Chaos [cph] (Author of 'kvec.h', a generic dynamic array)

License: GPL-3

Uses: Matrix

Released about 1 month ago.