EpiModel: AnRPackage for Mathematical Modeling of Infectious Disease over Networks

Abstract

EpiModel provides tools for building, simulating, and analyzing mathematical models for the population dynamics of infectious disease transmission in R. Several classes of models are included, but the unique contribution of this software package is a general stochastic framework for modeling the spread of epidemics on networks. This framework integrates recent advances in statistical methods for network analysis, temporal exponential random graph models, which allows the epidemic modeling to be firmly grounded in empirical data on the contacts and persistent partnerships that can spread infection. This article provides an overview of both the modeling tools built into EpiModel, designed to facilitate learning for students new to modeling, and the application programming interface for extending EpiModel, designed to facilitate the exploration of novel research questions for advanced modelers.

Impact of the Centers for Disease Control's HIV Preexposure Prophylaxis Guidelines for Men Who Have Sex With Men in the United States

“…The models in this study explicitly represented the CDC guidelines' behavioral indications for MSM based on the unique aspects of their dynamic sexual partnership networks, using robust statistical and mathematical modeling methods [17,19]. The complex structure of main, casual, and one-time sexual MSM partnership networks in which HIV infection risk occurs contributes to the high prevalence of HIV among MSM in the United States [18] and will be critical to target for any intervention seeking to mitigate that epidemic [20].…”

“…Parameters for sexual behavior were drawn from 2 empirical studies of MSM in Atlanta, Georgia [16]. Our model was built and simulated using the open-source EpiModel (version 1.2.5) software package (http://epimodel.org) for the R statistical computing platform [17]. The full methodological framework for these mathematical models, including the statistical estimation of dynamic network models, model parameterization, simulation, and data analysis, are provided in a Supplementary Appendix.…”

Covariation between the physiological and behavioral components of pathogen transmission: host heterogeneity determines epidemic outcomes

“…Likewise, for negative covariation, less infectious individuals decreased their expected mean degree to 2, and highly infectious individuals decreased their expected mean degree to 0 (Table 3). In the EpiModel package, this was achieved by using infection status itself as a network statistic via the 'nodefactor' term for simulating the dynamic network (Jenness et al 2016a). This term of the model allows different sub-groups of the population to have heterogeneity in their attributesin this case, mean degree (Jenness et al 2016a).…”

“…3.3.2,  www.rproject.org ) using self-written modules in the 'EpiModel' package (ver. 3.4.0,  www.epimodel.org/ ) (Jenness et al 2016a). The EpiModel package provides a suite of prewritten and modifiable functions for simulating infectious disease dynamics, including stochastic network models that rely on temporal ERGMs from the 'statnat' package.…”

Addressing Gaps in HIV Preexposure Prophylaxis Care to Reduce Racial Disparities in HIV Incidence in the United States

“…We previously developed a robust mathematical model for HIV/STI transmission dynamics for US MSM using the EpiModel software platform (www.epimodel.org) [28], a generalized toolkit for simulating epidemics over complex sexual networks under the statistical framework of temporal exponential random graph models (TERGMs) [29]. Our prior applications investigated the sources of HIV racial disparities among MSM in Atlanta and the potential impact of PrEP for MSM in the US [30,31].…”