Progress in HIV (human immunodeficiency virus) prevention interventions has been made in recent years. These developments have raised questions concerning the impact and optimal combination of biomedical and behavioral interventions. Agent-based models offer a way to compare intervention combinations that could not be readily accomplished with large scale prevention trials because of cost and logistical considerations. An agent-based model is a community of individuals whose behavior and interactions are simulated according to well- defined rules.

We describe two agent-based social network models and methods of calibration to sexual contact parameters relevant to MSM (men who have sex with men) in South Africa: a model with static partnerships and a novel model featuring self-reinforcing partnership edges. We only have sexual contact summary statistics (i.e. coarse network statistics) and these models are calibrated to replicate these statistics. A calibration approach was developed using recent advances in multi-objective optimization and applied to the self-reinforcing model.

We use these models to evaluate the varying impact of HIV prevention combinations by simulating community-randomized trials across realistic values of several parameters: ART (anti-retroviral therapy) uptake, PREP (pre-exposure prophylaxis) uptake, HIV testing uptake, and CAI (condomless anal intercourse) reduction. For the static partnership agent-based model, we create a statistical model to describe quantitatively the effects of these parameters and variation. For the self-reinforcing model we model sensitivity across several key parameters. We develop sample size and power formula for community randomized trials that incorporate estimates of variation and effect sizes determined from the agent-based model. We find that traditional sample size approaches that rely on binomial (or Poisson) models are inadequate and can lead to underpowered studies. In our studies of variation and effect sizes, we identified important sensitivities to the network contact structure. We conclude that agent-based models offer a useful tool in the design of HIV prevention trials.