ReviewFrontiers in epidemiology2025
Incorporating social determinants of health into agent-based models of HIV transmission: methodological challenges and future directions.
Review in Frontiers in epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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