Keywords: mathematical model, infectious disease, epidemiology, networks, R
Webpages:
https://CRAN.R-project.org/package=EpiModel,
http://epimodel.org/ The
EpiModel package provides tools for building, simulating, and analyzing mathematical models for epidemics using
R. Epidemic models are a formal representation of the complex systems that collectively determine the population dynamics of infectious disease transmission: contact between people, inter-host infection transmission, intra-host disease progression, and the underlying demographic processes. Simulating epidemic models serves as a computational laboratory to gain insight into the dynamics of these disease systems, test empirical hypotheses about the determinants of a specific outbreak patterns, and forecast the impact of interventions like vaccines, clinical treatment, or public health education campaigns.
A range of different modeling frameworks has been developed in the field of mathematical epidemiology over the last century. Several of these are included in
EpiModel, but the unique contribution of this software package is a general stochastic framework for modeling the spread of epidemics across dynamic contact networks. Network models represent repeated contacts with the same person or persons over time (e.g., sexual partnerships). These repeated contacts give rise to persistent network configurations – pairs, triples, and larger connected components – that in turn may establish the temporally ordered pathways for infectious disease transmission across a population. The timing and sequence of contacts, and the transmission acts within them, is most important when transmission requires intimate contact, that contact is relatively rare, and the probability of infection per contact is relatively low. This is the case for HIV and other sexually transmitted infections.
Both the estimation and simulation of the dynamic networks in
EpiModel are implemented using Markov Chain Monte Carlo (MCMC) algorithm functions for exponential-random graph models (ERGMs) from the
statnet suite of
R packages. These MCMC algorithms exploit a key property of ERGMs: that the maximum likelihood estimates of the model parameters uniquely reproduce the model statistics in expectation. The mathematical simulation of the contact network over time is theoretically guaranteed to vary stochastically around the observed network statistics. Temporal ERGMs provide the only integrated, principled framework for both the estimation of dynamic network models from sampled empirical data and also the simulation of complex dynamic networks with theoretically justified methods for handling changes in population size and composition over time.
In this talk, I will provide an overview of both the modeling tools built into
EpiModel, designed to facilitate learning for students new to modeling, and the package’s application programming interface (API) for extending
EpiModel, designed to facilitate the exploration of novel research questions for advanced modelers. I will motivate these research-level extensions by discussing our recent applications of these network modeling statistical methods and software tools to investigate the transmission dynamics of HIV and sexually transmitted infections among men who have sex with men in the United States and heterosexual couples in Sub-Saharan Africa.
