About

Aditya Khanna is an Associate Professor of Behavioral and Social Sciences at Brown University, specializing in computational social science with a focus on modeling biobehavioral health systems to inform policy initiatives aimed at achieving health equity. His research primarily employs network analysis, agent-based modeling, and predictive analytics to address public health challenges, including HIV prevention among sexual and gender minorities, substance use behaviors, incarceration, breast cancer screening, and COVID-19. Khanna's work aims to fill gaps in empirical data by integrating diverse sources to create model-based representations of real-world systems, thereby generating insights into components of public health systems that are less understood. He has led modeling efforts within multidisciplinary teams of behavioral scientists, clinicians, epidemiologists, statisticians, biologists, and community stakeholders, contributing to initiatives such as the Getting to Zero HIV Elimination project in Illinois and HIV elimination efforts in Los Angeles and Houston. His background includes a Ph.D. in Quantitative Ecology and an M.S. in Statistics from the University of Washington, with postdoctoral training in Global Health at the University of Washington and Infectious Diseases at the University of Chicago. Khanna's approach connects ecological modeling principles with public health, considering behavioral dynamics, age stratification, network structures, and migration processes, with a recent emphasis on public health equity issues like incarceration, addiction, and care interruptions.

Research topics

• Information Retrieval
• Machine Learning
• Computer Science
• Data Mining
• Artificial Intelligence
• Medicine
• Biology
• Physical therapy
• Nursing
• Internal medicine
• Family medicine
• Data science

Selected publications

• Uncertainty Aware Decision Support with Computationally Expensive Simulation Models: A Case Study of HIV Intervention Scenarios

  medRxiv · 2026-04-17

  articleOpen access

  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.

  PublisherOA PDFDOI

• Newly Diagnosed Individuals in Molecular HIV-1 Clusters in Rhode Island Over 3 Decades

  The Journal of Infectious Diseases · 2025-06-12

  articleOpen access

  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.

  PublisherOA PDFDOI

• Initial and Subsequent Engagement of Recently Diagnosed Persons Living with HIV in Contact Tracing Interviews Conducted by Public Health Practitioners

  AIDS and Behavior · 2025-06-16

  articleOpen access
  PublisherOA PDFDOI

• Enumeration of Even Dimensional Partitions modulo 4

  ArXiv.org · 2025-11-15

  preprintOpen access1st authorCorresponding

  The number of standard Young tableaux possible of shape corresponding to a partition $λ$ is called the dimension of the partition and is denoted by $f^λ$. Partitions with odd dimensions were enumerated by McKay and were further characterized by Macdonald using the theory of 2-core towers. We use the same theory to extend the results to partitions of $n$ with dimensions congruent to 2 modulo 4 which are enumerated by $a_2(n)$. We provide explicit results for $a_2(n)$ when $n$ has no consecutive 1s in its binary expansion and give a recursive formula to compute $a_2(n)$ for all $n$.

  PublisherOA PDFDOI

• Incorporating social determinants of health into agent-based models of HIV transmission: methodological challenges and future directions

  Frontiers in Epidemiology · 2025-02-27 · 1 citations

  reviewOpen access

  There is much focus in the field of HIV prevention research on understanding the impact of social determinants of health (e.g., housing, employment, incarceration) on HIV transmission and developing interventions to address underlying structural drivers of HIV risk. However, such interventions are resource-intensive and logistically challenging, and their evaluation is often limited by small sample sizes and short duration of follow-up. Because they allow for both detailed and large-scale simulations of counterfactual experiments, agent-based models (ABMs) can demonstrate the potential impact of combinations of interventions that may otherwise be infeasible to evaluate in empirical settings and help plan for efficient use of public health resources. There is a need for computational models that are sufficiently realistic to allow for evaluation of interventions that address socio-structural drivers of HIV transmission, though most HIV models to date have focused on more proximal influences on transmission dynamics. Modeling the complex social causes of infectious diseases is particularly challenging due to the complexity of the relationships and limitations in the measurement and quantification of causal relationships linking social determinants of health to HIV risk. Uncertainty exists in the magnitude and direction of associations among the variables used to parameterize the models, the representation of sexual transmission networks, and the model structure (i.e. the causal pathways representing the system of HIV transmission) itself. This paper will review the state of the literature on incorporating social determinants of health into epidemiological models of HIV transmission. Using examples from our ongoing work, we will discuss Uncertainty Quantification and Robust Decision Making methods to address some of the above-mentioned challenges and suggest directions for future methodological work in this area.

  PublisherOA PDFDOI

• Brief Report: Long-Acting Injectable PrEP Can Substantially Reduce HIV Incidence in Los Angeles County: A Simulation Study

  JAIDS Journal of Acquired Immune Deficiency Syndromes · 2025-10-30

  articleOpen access1st author

  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.

  PublisherOA PDFDOI

• Integrating Partner Services and Molecular Epidemiology Data to Enhance HIV Transmission Disruption in Rhode Island

  Open Forum Infectious Diseases · 2025-06-11 · 1 citations

  articleOpen access1st authorCorresponding

  Objectives: 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.

  PublisherOA PDFDOI

• Combined self-stigma of justice system involvement, opioid use, and mental health disorder.

  Stigma and Health · 2025-04-07

  article
  PublisherDOI

• Integrating HIV Cluster Analysis in Everyday Public Health Practice: Lessons Learned From a Public Health–—Academic Partnership

  JAIDS Journal of Acquired Immune Deficiency Syndromes · 2024-06-12 · 3 citations

  articleOpen access

  BACKGROUND: The use of molecular HIV cluster analysis to supplement public health contact tracing has shown promise in addressing HIV outbreaks. However, the potential of HIV cluster analysis as an adjunct to daily, person-by-person HIV prevention efforts remains unknown. We documented lessons learned within a unique public health-academic partnership while guiding workaday HIV prevention efforts with near-real-time molecular cluster analysis. SETTING: A public health-academic partnership in the State of Rhode Island, the United States. METHODS: We recorded perceptions of our team of academicians and public health practitioners that were encountered in an 18-month study evaluating the integration of molecular cluster analysis with HIV contact tracing for public health benefit. The focus was on monthly conferences where molecular clustering of each new statewide diagnosis was discussed to facilitate targeted interventions and on attempted reinterviews of all newly HIV-diagnosed persons statewide whose HIV sequences clustered to increase partner naming. RESULTS: Three main themes emerged: First, multidisciplinary conferences are substantially beneficial for gleaning actionable inferences from integrating molecular cluster analysis and public health data. Second, universal reinterviews were perceived to potentially have negative consequences but may be selectively beneficial. Third, the translation of cluster analysis into public health action is hampered by jurisdictional surveillance boundaries and within-jurisdictional data silos, across which data sharing is problematic. CONCLUSIONS: Insights from a statewide public health-academic partnership support integration of molecular HIV cluster analyses with public health efforts, which can guide public health activities to prevent transmission while identifying substantial barriers to integration, informing continued research.

  PublisherOA PDFDOI

• Social network dynamics of tobacco smoking and alcohol use among persons involved with the criminal legal system (PCLS): A modeling study

  The International Journal of Alcohol and Drug Research · 2024-10-01

  articleOpen access1st authorCorresponding

  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.

  PublisherOA PDFDOI

Recent grants

• Integrated Network Analysis of RADx-UP Data to Increase COVID-19 Testing and Vaccination Among Persons Involved with Criminal Legal Systems (PCLS)

  NIH · $487k · 2023–2026

• Administrative Core

  NIH · $34.9M · 2019–2029

• MODELING COMBINATION BIOMEDICAL SUBSTANCE USE INTERVENTIONS TO INFORM A GETTING TO ZERO IMPLEMENTATION FOR BLACK MEN WHO HAVE SEX WITH MEN

  NIH · $162k · 2021–2021

Frequent coauthors

• John A. Schneider

  University of Chicago

  96 shared

• Nina T. Harawa

  Charles R. Drew University of Medicine and Science

  30 shared

• Kayo Fujimoto

  The University of Texas Health Science Center at Houston

  27 shared

• Anna Hotton

  University of Chicago

  22 shared

• Jonathan Ozik

  University of Chicago

  21 shared

• Russell Brewer

  University of Chicago

  18 shared

• Yamilé Molina

  University of Illinois Chicago

  18 shared

• Nyahne Q. Bergeron

  University of Illinois Chicago

  17 shared

Labs

• Aditya Khanna's LabPI

  Not provided

Education

• Ph.D.

  University of Washington

  2012