Advancing Methods in Infectious Diseases Models: Incorporating Structural Causes

PROJECT ABSTRACT This research program aims to develop novel modeling methods, tools, and guidelines to incorporate racialized lived experiences into mathematical models of infectious disease transmission by explicitly modeling structural drivers of racial disparities in infectious disease exposure, susceptibility and severity, and consequences. In particular, this research will intentionally engage with geographic disparities in the United States through geographic information systems (GIS) coded data to highlight the importance of social context and determinants across the life course to the transmission of infectious diseases. We will employ systems science to analyze in silico simulations and post-hoc data analysis of simulation output to understand the structural drivers of infectious disease disparities. In silico simulation allows for the development of synthetic populations that represent individuals and households (and their characteristics) within a particular geographic area. We plan to modify the model structure to explore the impact and specificity gained by adding a variety of model characteristics, including stochasticity, natural history, and environmental influence. We then aim to perform comprehensive sensitivity analyses accounting for social and political context and the incorporation of multiple interacting factors that may help identify patterns in spread of particular disease types. Ultimately, the goal of the in silico simulations is to mathematically link policy effects to health outcomes through racialized lived experiences (represented and parameterized as agent characteristics). While the modeling frame will be flexible, we will use data on SARS-CoV-2 and influenza as two examples to demonstrate the feasibility of the methods we develop. The results from this work will allow us to develop policy recommendations for structural interventions to reduce racial disparities in infectious disease outcomes. Incorporating structural interventions into the model structure will require flexibility to account for the interference and feedback with individual behaviors. The structural interventions we plan to examine using in silico simulations include eliminating residential segregation, increasing accessibility to stable housing, reducing income inequality, and distribution of healthy food choices represented by real-world programs across the United States. This research will lay the groundwork to inform ongoing control of existing and emerging infectious disease pathogens and prevent the unequal health- and cost-related burdens on communities of color.