PopART-IBM

PopART-IBM simulates HIV epidemic dynamics in high-prevalence settings using an individual-based model tailored to the HPTN 071 (PopART) trial.

Key Features:

• Individual-Based Modeling: Explicitly models individual sexual partnerships and incorporates behavioral and biological heterogeneities within sexual networks for detailed transmission representation.
• Computational Efficiency: Capable of simulating 50 years of an HIV epidemic in a large community in under one minute.
• Calibration and Bayesian Inference: Employs a Bayesian inference framework to calibrate against age- and sex-stratified data from multiple sources, including HIV prevalence, awareness of HIV status, antiretroviral therapy (ART) status, and viral suppression in the study community in Zambia.
• Projections and Scenario Analysis: Produces future projections of HIV prevalence and incidence and evaluates counterfactual outcomes such as scenarios in the absence of trial interventions.

Scientific Applications:

• Transmission dynamics analysis: Enables detailed study of HIV transmission within sexual networks accounting for behavioral and biological heterogeneities.
• Intervention scenario evaluation: Allows exploration of intervention scenarios and their impacts on epidemic trajectories, including counterfactuals relevant to HPTN 071 (PopART).
• Epidemic projection and planning: Generates long-term projections of HIV prevalence and incidence to inform public health planning and policy in high-prevalence settings.
• Data-integrated inference: Integrates age- and sex-stratified empirical data (prevalence, status awareness, ART, viral suppression) to produce community-specific calibrated inferences.

Methodology:

Explicit individual-based modeling of behaviors and sexual partnerships combined with Bayesian inference calibration to age- and sex-stratified data (HIV prevalence, awareness of status, ART status, viral suppression) from multiple sources in the study community in Zambia.