Simulating Modeling to Understand and Address HIV Disparities in Racial Ethnic and Sexual Minority Populations (NIH)

Funded by the National Institute of Health

Award Number 1R01MD014703-01A1

Racial, ethnic, and sexual minority populations are disproportionately impacted by infectious disease, particularly HIV. Individuals at the intersection of multiple of these marginalized identities are even more likely to be impacted by HIV – especially Black and Hispanic men who have sex with men. While there is growing evidence that the day-to-day life of racial and sexual minorities differs from majority populations, there is a limited understanding of how differences in neighborhoods, differences in places where time is spent, and differences in the kinds of people connected with may impact disease spread and fuel disparities. Moreover, there is even less comprehensive understanding of how public health strategies could be refined to specifically reduce health disparities.

Simulation models that accurately replicate population dynamics by simulating the movement and interaction of millions of individuals allow researchers a toolbox to understand the underlying dynamics of disease transmission and identify potential targets for intervention.

This project joins two complementary teams of researchers in Chicago to build chiSTIG, a simulation model specifically derived to understand the social contextual dynamics which lead to disparities in HIV.

  • The first team, at Northwestern University, have been funded by the NIH to capture rich data on the social systems and physical spaces inhabited by racial and sexual minorities, and have utilized these data to understand how the social and sexual isolation of young Black men who have sex with men (BMSM) in Chicago drives disparities in HIV.
  • The second team, at the University of Chicago/Argonne National Laboratory, have built chiSIM, an extraordinarily powerful agent-based modeling (ABM) framework that simulates the interaction of 2.9 million Chicagoans across 1.2 million geo-located places to understand disease outbreaks and guide intervention development. chiSIM is a flexible system that has been used to understand prevention strategies for a number of infectious diseases.

We propose to utilize several existing rich empirical datasets to build a chiSIM-derived model, chiSTIG, so that it might serve as a counterfactual laboratory able to test competing hypotheses regarding the etiology of infectious disease inequities in HIV for sexual minorities, specifically Black and Hispanic MSM, and potential routes for intervention. Specifically, the framework will be extended through the integration of detailed data on the physical and online third places utilized by and the social and sexual interactions of racially diverse MSM in Chicago.

Investigators: 

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