SimInf: A Framework for Data-Driven Stochastic Disease Spread...
In SimInf: A Framework for Data-Driven Stochastic Disease Spread Simulations

SimInf

R Documentation

A Framework for Data-Driven Stochastic Disease Spread Simulations

Description

The SimInf package provides a flexible framework for data-driven spatio-temporal disease spread modeling, designed to efficiently handle population demographics and network data. The framework integrates infection dynamics in each subpopulation as continuous-time Markov chains (CTMC) using the Gillespie stochastic simulation algorithm (SSA) and incorporates available data such as births, deaths or movements as scheduled events. A scheduled event is used to modify the state of a subpopulation at a predefined time-point.

Details

The SimInf_model is central and provides the basis for the framework. A SimInf_model object supplies the state-change matrix, the dependency graph, the scheduled events, and the initial state of the system.

All predefined models in SimInf have a generating function, with the same name as the model, for example SIR.

A model can also be created from a model specification using the mparse method.

After a model is created, a simulation is started with a call to the run method and if execution is successful, it returns a modified SimInf_model object with a single stochastic solution trajectory attached to it.

SimInf provides several utility functions to inspect simulated data, for example, show, summary and plot. To facilitate custom analysis, it provides the trajectory,SimInf_model-method and prevalence methods.

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. To support this, SimInf has functionality to generate the required C and R code from a model specification, see package_skeleton