Aditya Subhash Khanna

Detailed agent-based simulations are increasingly used to support policy decisions, but their computational cost and complex uncertainty structure make systematic scenario analysis challenging. We present a data-driven, uncertainty-aware decision support (DDUADS) workflow for using stochastic simulation models as decision-support tools under limited computational budgets. The approach combines several established techniques—sensitivity screening, Bayesian calibration using simulation-based inference, and multi-surrogate model integration for translational efficiency—into a coherent pipeline that enables uncertainty-aware policy analysis. Rather than producing a single baseline, the calibration stage yields a posterior distribution over plausible model parameterizations, allowing flexible, uncertainty-aware forward projections. We demonstrate the DDUADS workflow on the INFORM-HIV agent-based model of HIV transmission in Chicago to evaluate potential disruptions in antiretroviral therapy (ART) and pre-exposure prophylaxis (PrEP) use. While the specific application is HIV modeling, the challenges and techniques described here arise in other simulation studies and can be applied to decision support in other domains.

Background: Although oral pre-exposure prophylaxis (PrEP) has been instrumental in decreasing HIV incidence, its daily dosing regimen poses adherence challenges. Using an agent-based network model informed by empirical data, we simulate the impact of introducing long-acting injectable (LAI) PrEP among young Black men who have sex with men (YBMSM) in Los Angeles County, a group disproportionately affected by HIV. Setting: Computer simulations using an agent-based network model. Methods: We modeled HIV transmission among YBMSM over 10 years under scenarios varying the proportion of PrEP users opting for LAI instead of oral medications and adherence levels to LAI retention. The model was calibrated with empirical data and included dynamic sexual networks, HIV progression, and biomedical interventions. Results: Modeling showed that LAI PrEP substantially reduced HIV incidence and prevalence over 10 years compared with oral PrEP alone. Scenarios with LAI retention (ie, continued use across bimonthly dosing cycles) rates of 60% or higher resulted in reductions comparable with or exceeding those achieved by oral PrEP, with up to a 45% decrease in HIV incidence observed when all PrEP users switched to LAI and retention reached 85%. Conclusions: Long-acting injectable PrEP offers significant potential to advance HIV prevention efforts among YBMSM by addressing adherence challenges inherent to oral PrEP. Integrating LAI into public health initiatives may yield substantial reductions in HIV incidence, contributing to ending the HIV epidemic among this high-priority population.

Background: Characterizing clustering rates of people with HIV in high-risk populations can offer insights on the HIV epidemic, enhancing efforts to control its spread. Methods: We investigated longitudinal dynamics of clustering rates among individuals newly diagnosed with HIV-1. Data were extracted from the medical records of all people with HIV in Rhode Island with available viral sequences. Partial pol sequences were grouped by HIV diagnosis year, and clusters were identified in annual phylogenies. Clustering trends were estimated within 11 sociodemographic variables with the Mann-Kendall statistic. Associations with clustering propensity and changes over time were tested via generalized linear mixed effects models. Results: HIV-1 sequences from 2630 individuals representing the statewide epidemic were analyzed across 33 annual datasets (1991–2023). Over this period, a continuous increase in clustering rates among newly diagnosed individuals was observed despite decreasing diagnoses over the last decade. Significant upward trends in clustering were seen among newly diagnosed men who have sex with men, males, the 21- to 40-year age group, non–Hispanic or Latino people, White persons, those with subtype B, and US-born individuals but not among people who inject drugs, females, and incarcerated individuals. Analyses of relative associations between groups within variables corroborated these results. Conclusions: Analyses focusing on molecular HIV clusters among newly diagnosed people in a statewide epidemic over 3 decades revealed significant evolving trends among those at highest risk of HIV transmission, patterns not seen in the overall population. These findings inform the design and development of targeted public health interventions aimed at high-risk populations to curb HIV spread.

Objective(s): Evaluate added value of integrating partner services and molecular epidemiology data to disrupt HIV transmission. Design: Integration of statewide partner services and molecular databases. Methods: We evaluated overlap of persons and their social/molecular links in contact tracing (Contact Tracing Database [CTDB], 2008–2022) and HIV-1 genomic (Genomic Database [GDB], 2004–2023) databases using Jaccard coefficient (JC); inferred molecular clustering using phylogeny; assessed care engagement gaps by developing a “partner naming” cascade; and explored associations of molecular clustering and partner naming using generalized estimating equations. Results: Among 2418 CTDB and 2527 GDB individuals, 894 (JC = 0.22) and 59 links (JC = 0.012) appeared in both databases, demonstrating low overlap. Molecular clustering occurred in 48% of all GDB persons, 65% of persons in both databases, 71% of persons providing partner data, and 88% of named partners in both databases. Of 1342 named partners, contacts were attempted for 66%, and 93% were reached; of those reached, 71% were newly HIV-tested, of whom 27% were newly diagnosed, and all newly diagnosed were sequenced. Men who have sex with men and people who inject drugs were more likely to cluster molecularly in the GDB if linked in the CTDB, while high-risk heterosexuals were less likely. Men who have sex with men and older individuals were more likely to be linked in the CTDB if they clustered molecularly, while people who inject drugs were less likely. Conclusions: Comprehensive, statewide integration of contact tracing and molecular data enables public health insights not available with one source alone, underscoring the added value of data integration in identifying gaps to improve HIV prevention services.

Background: Tobacco smoking and alcohol use contribute to a synergy of epidemics (a “syndemic”) that disproportionately affects persons involved with the criminal legal system (PCLS) and their social networks. An improved understanding of the complex interrelationships among the factors of the incarceration-tobacco-alcohol syndemic is essential to develop effective reform policies and interventions. However, collecting empirical data on these interrelationships is often hampered due to logistical and ethical challenges. Methods: We developed an agent-based network model (ABNM) to simulate the effects of the incarceration-tobacco-alcohol syndemic in the state of Rhode Island, USA. The model was validated and calibrated using empirical survey and demographic data. Outcomes included current smoking and heavy alcohol use rates in the first year after release among previously incarcerated agents and in their social networks. Results: The model successfully replicated demographic, substance use, and incarceration-related parameters. Simulation results suggest high rates of smoking (approximately 80% currently smoking persons in the first few weeks after release) and heavy alcohol use (approximately 40% current heavy alcohol use rate in the first few weeks after release) among PCLS, especially persons with multiple incarceration events. The model also estimated elevated rates of current smoking and current heavy alcohol use in the direct social contacts of PCLS. Discussion: This ABNM integrates biobehavioral health processes relating to incarceration and substance use. This model can be used as a platform to evaluate the potential impacts of interventions provided to PCLS and their networks

In a cross-sectional study, we examined the social network characteristics of COVID-19 vaccination in adult participants from 5 states in the US Northeast using generalized estimating equation (GEE) models. Health behaviors, including vaccine acceptance and hesitancy, are determined by an interplay of individual and social network factors. The influence of social network determinants are not well understood with regard to vaccination behaviors. We collected cross-sectional data on vaccination and adherence to COVID-19 guidelines self-reported by individuals for themselves and their 5 closest social network contacts to understand the impact of network determinants.

Context: Medicaid expansion has been nationally shown to improve engagement in the human immunodeficiency virus (HIV) treatment and prevention continua, which are vital steps to stopping the HIV epidemic. New HIV infections in the United States are disproportionately concentrated among young Black men who have sex with men (YBMSM). Houston, TX, is the most populous city in the Southern United States with a racially/ethnically diverse population that is located in 1 of 11 US states that have not yet expanded Medicaid coverage as of 2021. Methods: An agent-based model that incorporated the sexual networks of YBMSM was used to simulate improved antiretroviral treatment and pre-exposure prophylaxis (PrEP) engagement through Medicaid expansion in Houston, TX. Analyses considered the HIV incidence (number of new infections and as a rate metric) among YBMSM over the next 10 years under Medicaid expansion as the primary outcome. Additional scenarios, involving viral suppression and PrEP uptake above the projected levels achieved under Medicaid expansion, were also simulated. Results: The baseline model projected an HIV incidence rate of 4.96 per 100 person years (py) and about 368 new annual HIV infections in the 10th year. Improved HIV treatment and prevention continua engagement under Medicaid expansion resulted in a 14.9% decline in the number of annual new HIV infections in the 10th year. Increasing viral suppression by an additional 15% and PrEP uptake by 30% resulted in a 44.0% decline in new HIV infections in the 10th year, and a 27.1% decline in cumulative infections across the 10 years of the simulated intervention. Findings: Simulation results indicate that Medicaid expansion has the potential to reduce HIV incidence among YBMSM in Houston. Achieving HIV elimination objectives, however, might require additional effective measures to increase antiretroviral treatment and PrEP uptake beyond the projected improvements under expanded Medicaid.