README.md

In statnet/EpiModel: Mathematical Modeling of Infectious Disease Dynamics

EpiModel

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 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 easy application programming interface (API) for extending these templates to address novel scientific research aims.

Lead Authors

| | | | |---------------------------------------------------------------|-----------------------------------------|--------------------------| | Samuel M. Jenness | Department of Epidemiology | Emory University | | Steven M. Goodreau | Department of Anthropology | University of Washington | | Martina Morris | Departments of Statistics and Sociology | University of Washington | | Adrien Le Guillou | Department of Epidemiology | Emory University | | Chad Klumb | Center for Studies in Demography and Ecology | University of Washington |

Additional contributors to EpiModel are listed on the contributors page.

Installation

The current release version can be found on CRAN and installed with:

install.packages("EpiModel", dependencies = TRUE)

To install this development version, use the remotes package:

if (!require("remotes")) install.packages("remotes")
remotes::install_github("EpiModel/EpiModel")

Documentation and Support

Website. The main website for EpiModel, with tutorials and other supporting files, is here: http://www.epimodel.org/.

Methods Paper. A good place to start learning about EpiModel is the main methods paper published in the Journal of Statistical Software. It is available at: https://doi.org/10.18637/jss.v084.i08.

Summer Course. Network Modeling for Epidemics is our annual 5-day course at the University of Washington where we teach the statistical theory, software tools, and applied modeling methods using EpiModel. Our course materials are open-source and updated annually around the time of the course.

Getting Help. Users are encouraged to use Github issues on this repository as a place to ask questions (both technical coding questions and conceptual modeling questions), report bugs, and request new features & functionality. Broader modeling questions can be posted on the Discussions board here.

The EpiModel Gallery

The EpiModel Gallery contains templates of extensions to EpiModel, for now focused on network-based mathematical modeling class. We will be continuing to add new examples the gallery, and encourage users to either file requests for new examples or else to contribute them directly.

Citation

If using EpiModel for teaching or research, please include a citation our main 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

Please also send us an email if you have used EpiModel in your work so we can add the citation below.

Funding

The primary support for the development of these software tools and statistical methods has been by two National Institutes of Health (NIH) grants. Our applied research projects using EpiModel have received funding from the NIH and Centers for Disease Control and Prevention (CDC). Our team also receives institutional support through center-level NIH grants. A full list of our funding support can be found here.

EpiModel in the Scientific Literature

EpiModel and its extension packages have been used in the following scientific journal articles. A list of these articles can be accessed in a wiki page or on Zotero. (If you are aware of others, send us an email at samuel.m.jenness\@emory.edu to be included in this list.)

Copyright

These materials are distributed under the GPL-3 license, with the following copyright and attribution requirements listed in the LICENSE document above.

statnet/EpiModel documentation built on March 30, 2024, 11:26 a.m.