vers <- packageVersion("EpiModel") year <- format(Sys.time(), "%Y")
The EpiModel package provides tools for simulating mathematical models of infectious disease dynamics. Epidemic model classes include deterministic compartmental models, stochastic individual-contact models, and stochastic network models. Network models use the robust statistical methods of temporal exponential-family random graph models (ERGMs) from the Statnet suite of software packages in R. Standard templates for epidemic modeling include SI, SIR, and SIS disease types. EpiModel features an API for extending these templates to address novel scientific research aims.
This vignette provides a general orientation to the EpiModel tutorials and documentation within the package and hosted elsewhere online. Most full-length tutorials may be found at the EpiModel website (http://www.epimodel.org), which also points to our Network Modeling for Epidemics (NME) short-course (https://statnet.org/nme/).
Within the package, you can consult the extensive help documentation for each exported function:
help(package = "EpiModel")
To see the latest updates to EpiModel, consult the
NEWS file in the package, which is also summarized on our Github Releases (https://github.com/EpiModel/EpiModel/releases).
If you are interested in the stochastic network model class, we suggest learning about using EpiModel with the following sequence:
Some of the latest developments in EpiModel are related to working with network model inputs and outputs, which are covered in these advanced topics vignettes within the package:
Working With Model Parameters
Any technical coding questions, non-technical conceptual modeling questions, or EpiModel feature requests may be posted as a Github issues at our main Github repository (https://github.com/EpiModel/EpiModel/issues).
If using EpiModel for teaching or research, please include a citation to our primary methods paper:
Jenness SM, Goodreau SM and Morris M. EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks. Journal of Statistical Software. 2018; 84(8): 1-47. doi: 10.18637/jss.v084.i08 (https://doi.org/10.18637/jss.v084.i08).
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