Recorded 06 March 2025. Sara Del Valle of Los Alamos National Laboratory presents "Why population heterogeneity matters for modelling infectious diseases: implications for health equity" at IPAM's LatMath 2025 Workshop. Abstract: The COVID-19 pandemic highlighted significant health disparities among sociodemographic groups in the United States, underscoring the need for modelling approaches that can capture the complex dynamics driving these disparities. Specifically, variations in case incidence, mortality, and disease burden have been observed across historically marginalized racial and ethnic groups, as well as by demographic and geographic regions. Accurately incorporating fine-grained sociodemographic attributes into infectious disease models remains challenging due to complex correlations among individual characteristics within populations. Additionally, embedding mechanistic representations of exposure disparities requires a nuanced understanding of variation in exposure risk across different transmission settings. In this study, we address these challenges by incorporating drivers of exposure risk and detailed sociodemographic data into EpiCast—a large-scale agent-based model of respiratory pathogen spread in the United States. Our findings show that embedding high levels of population heterogeneity into models of infectious disease can reveal inequitable outcomes in disease burden driven by factors such as household size and workplace exposure. This approach can be used to better inform policy interventions to mitigate inequity during future pandemics. Learn more online at: https://www.ipam.ucla.edu/programs/special-events-and-conferences/latmath-2025/?tab=overview