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.
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