SimInf {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` 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`

[Package SimInf version 6.3.0 Index]