Approximate inference for longitudinal mechanistic HIV contact network

TL;DR

This paper introduces a new method for modeling HIV transmission networks using mechanistic models and Bayesian inference, showing how longitudinal data improves accuracy.

Contribution

A novel ABC-based inference scheme for longitudinal mechanistic network models of HIV transmission.

Findings

Longitudinal study designs improve inference accuracy by up to 18% compared to cross-sectional designs.

The gains depend on the timing of data collection and the choice of summary statistics.

The method is applied to contact networks for men who have sex with men.

Abstract

Network models are increasingly used to study infectious disease spread. Exponential Random Graph models have a history in this area, with scalable inference methods now available. An alternative approach uses mechanistic network models. Mechanistic network models directly capture individual behaviors, making them suitable for studying sexually transmitted diseases. Combining mechanistic models with Approximate Bayesian Computation allows flexible modeling using domain-specific interaction rules among agents, avoiding network model oversimplifications. These models are ideal for longitudinal settings as they explicitly incorporate network evolution over time. We implemented a discrete-time version of a previously published continuous-time model of evolving contact networks for men who have sex with men and proposed an ABC-based approximate inference scheme for it. As expected, we found that a two-wave longitudinal study design improves the accuracy of inference compared to a cross-sectional design. However, the gains in precision in collecting data twice, up to 18%, depend on the spacing of the two waves and are sensitive to the choice of summary statistics. In addition to methodological developments, our results inform the design of future longitudinal network studies in sexually transmitted diseases, specifically in terms of what data to collect from participants and when to do so.

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