The Radiation-Transmission-Reception (RTR) model of propagation: Implications for the effectiveness of network interventions

Agent-based model projections for reducing HIV infection among MSM: Prevention and care pathways to end the HIV epidemic in Chicago, Illinois

Our objective is to improve local decision-making for strategies to end the HIV epidemic using the newly developed Levers of HIV agent-based model (ABM). Agent-based models use computer simulations that incorporate heterogeneity in individual behaviors and interactions, allow emergence of systemic behaviors, and extrapolate into the future. The Levers of HIV model (LHM) uses Chicago neighborhood demographics, data on sex-risk behaviors and sexual networks, and data on the prevention and care cascades, to model local dynamics. It models the impact of changes in local preexposure prophylaxis (PrEP) and antiretroviral treatment (ART) (ie, levers) for meeting Illinois' goal of "Getting to Zero" (GTZ) -reducing by 90% new HIV infections among men who have sex with men (MSM) by 2030. We simulate a 15-year period (2016-2030) for 2304 distinct scenarios based on 6 levers related to HIV treatment and prevention: (1) linkage to PrEP for those testing negative, (2) linkage to ART for those living with HIV, (3) adherence to PrEP, (4) viral suppression by means of ART, (5) PrEP retention, and (6) ART retention. Using tree-based methods, we identify the best scenarios at achieving a 90% HIV infection reduction by 2030. The optimal scenario consisted of the highest levels of ART retention and PrEP adherence, next to highest levels of PrEP retention, and moderate levels of PrEP linkage, achieved 90% reduction by 2030 in 58% of simulations. We used Bayesian posterior predictive distributions based on our simulated results to determine the likelihood of attaining 90% HIV infection reduction using the most recent Chicago Department of Public Health surveillance data and found that projections of the current rate of decline (2016-2019) would not achieve the 90% (p = 0.0006) reduction target for 2030. Our results suggest that increases are needed at all steps of the PrEP cascade, combined with increases in retention in HIV care, to approach 90% reduction in new HIV diagnoses by 2030. These findings show how simulation modeling with local data can guide policy makers to identify and invest in efficient care models to achieve long-term local goals of ending the HIV epidemic.

Leveraging Modularity During Replication of High-Fidelity Models: Lessons from Replicating an Agent-Based Model for HIV Prevention

High-fidelity models are increasingly used to predict, and guide decision making. Prior work has emphasized the importance of replication in ensuring reliable modeling, and has yielded important replication strategies. However, this work is based on relatively simple theory generating models, and its lessons might not translate to high-fidelity models used for decision support. Using NetLogo we replicate a recently published high-fidelity model examining the effects of a HIV biomedical intervention. We use a modular approach to build our model from the ground up, and provide examples of the replication process investigating the replication of two sub-modules as well as the overall simulation experiment. For the first module, we achieved numerical identity during replication, whereas we obtained distributional equivalence in replicating the second module. We achieved relational equivalence among the overall model behaviors, with a 0.98 correlation across the two implementations for our outcome measure even without strictly following the original model in the formation of the sexual network. Our results show that replication of high-fidelity models is feasible when following a set of systematic strategies that leverage the modularity, and highlight the role of replication standards, modular testing, and functional code in facilitating such strategies.