Network Models for HIV/STI Transmission Dynamics: Statistical Methods and Computational Tools

Introduction: Mechanistic or mathematical models represent the temporal evolution of epidemics at the population level as a consequence of behavior, biological, and other attributes of individuals or groups within that population. Several classes of mechanistic models have been used for HIV epidemiology, with deterministic compartmental models solved with ordinary differential equations still the predominant framework. Network modeling, however, is unique in its representation of repeated contacts between the same two persons over time, necessary to represent realistic social, sexual, and needle-sharing partnerships required for transmitting “close-contact” diseases like HIV and other sexually transmitted infections. Investigating network-based drivers of epidemics and opportunities for disease prevention that depend on network structure has required the development of statistical approaches to modeling dynamic network structures embedded within broader mathematical models of intra- and inter-host epidemiology, demography, and bio-behavioral disease transmission.

Methods: In this talk, I present on temporal exponential random graph models (TERGMs) to model dynamic networks using easily collected egocentric network data, the integration of these methods within our epidemic modeling software, EpiModel. 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.

Results: This talk will provide a historical background and motivation for the development of network modeling methods within this TERGM statistical framework and EpiModel software tool. We will demonstrate these methods in the context of our recent applied work on HIV prevention among men who have sex with men in the United States.