  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 (MSM) 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.
