Abstract
The COVID-19 pandemic highlighted significant differences in infectious disease burden among sociodemographic groups in the United States, underscoring the need for modelling approaches that can capture the complex dynamics driving these heterogeneities. Specifically, variation in case incidence, mortality and disease burden has been observed across subpopulations stratified by race, ethnicity, sex, age and geographic region. Accurately incorporating fine-grained sociodemographic attributes into infectious disease models remains challenging due to complex correlations among individual characteristics. Additionally, accurately modelling transmission while accounting for exposure differences among population strata requires a detailed understanding of transmission risk across interaction settings. 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. Using this model, we demonstrate how differences in the rate of infections between key demographic groups emerge in households, workplaces and schools. Our findings show that embedding fine-grained population heterogeneity into infectious disease models can reveal uneven outcomes in predicted disease burden among racial groups, driven by factors such as household size and workplace exposure risk. This study demonstrates the potential of detailed models of infectious disease spread to inform policy intervention design for future pandemics.
| Original language | English |
|---|---|
| Article number | 20250006 |
| Journal | Interface Focus |
| Volume | 15 |
| Issue number | 4 |
| DOIs | |
| State | Published - Sep 26 2025 |
Funding
This work was supported by the US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and by cooperative agreement CDC-RFA-FT-23-0069 from the CDC’s Center for Forecasting and Outbreak Analytics. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention. This work was performed at Los Alamos National Laboratory (LANL), an equal opportunity employer, which is operated by Triad National Security, LLC, for the National Nuclear Security Administration (NNSA) of the US Department of Energy (DOE) under contract 19FED1916814CKC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work is approved for public distribution under LA-UR-25-20349. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of LANL. This research used resources provided by the Darwin testbed at LANL which is funded by the Computational Systems and Software Environments subprogram of LANL’s Advanced Simulation and Computing program (NNSA/DOE). T.H. is supported by an Australian Government Research Training Program Scholarship and The University of Melbourne Elizabeth and Vernon Puzey Scholarship. Acknowledgements This work was supported by the US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and by cooperative agreement CDC-RFA-FT-23-0069 from the CDC's Center for Forecasting and Outbreak Analytics. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention. This work was performed at Los Alamos National Laboratory (LANL), an equal opportunity employer, which is operated by Triad National Security, LLC, for the National Nuclear Security Administration (NNSA) of the US Department of Energy (DOE) under contract 19FED1916814CKC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work is approved for public distribution under LA-UR-25-20349. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of LANL. This research used resources provided by the Darwin testbed at LANL which is funded by the Computational Systems and Software Environments subprogram of LANL's Advanced Simulation and Computing program (NNSA/DOE). T.H. is supported by an Australian Government Research Training Program Scholarship and The University of Melbourne Elizabeth and Vernon Puzey Scholarship. Acknowledgements. We would like to thank David J. Butts, Christa Brelsford and Jeffrey S. Keithley for helpful discussions during the course of this study. We would also like to thank the anonymous reviewers whose suggestions helped us to improve the manuscript.
Keywords
- epidemiology
- exposure risk
- respiratory pathogens
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