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