SimInf

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.

Last 30 days

This package has been downloaded 445 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 10 times.

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Last 365 days

This package has been downloaded 7,660 times in the last 365 days. Impressive! The kind of number that makes colleagues ask, 'How did you do it?' The day with the most downloads was Jun 19, 2024 with 113 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.