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>. The package also provides functionality to fit models to time series data using the Approximate Bayesian Computation Sequential Monte Carlo ('ABC-SMC') algorithm of Toni and others (2009) <doi:10.1098/rsif.2008.0172>.
|Author||Stefan Widgren [aut, cre] (<https://orcid.org/0000-0001-5745-2284>), Robin Eriksson [aut] (<https://orcid.org/0000-0002-4291-712X>), Stefan Engblom [aut] (<https://orcid.org/0000-0002-3614-1732>), Pavol Bauer [aut] (<https://orcid.org/0000-0003-4328-7171>), Thomas Rosendal [ctb] (<https://orcid.org/0000-0002-6576-9668>), Ivana Rodriguez Ewerlöf [ctb] (<https://orcid.org/0000-0002-9678-9813>), Attractive Chaos [cph] (Author of 'kvec.h'.)|
|Maintainer||Stefan Widgren <firstname.lastname@example.org>|
|Package repository||View on CRAN|
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