Multi-model insights into racial and ethnic heterogeneities in early COVID-19 transmission and severity in the U.S.
Year: 2026
Type: article
Abstract: The COVID-19 pandemic was marked by unequal disease burden across racial and ethnic groups in the US. Using a multi-model ensemble framework, we retrospectively assessed the impact of racial and ethnic heterogeneities in COVID-19 transmission and disease severity on mortality during the winter 2020–2021 surge in California and North Carolina. Models incorporating race and ethnicity projected mortality more accurately than null models, while the ensemble outperformed all individual models on average by weighted interval scores. Eliminating transmission heterogeneities produced larger mortality reductions than eliminating severity differences alone, particularly for Latino populations in California (55% reduction) and Black populations in North Carolina (28% reduction). Reducing severity heterogeneity also lowered mortality, with the greatest benefit among Black populations in both states (21% and 16% reductions in California and North Carolina, respectively). Eliminating both transmission and severity heterogeneity yielded the largest reductions overall, predicting 49–70% lower mortality across racial and ethnic groups in California and 23–40% in North Carolina. Mitigating transmission inequities also produced indirect benefits for non-targeted populations, highlighting the broad impact of reducing disparities in exposure risk and access to protective resources.
Source: American Journal of Epidemiology
DOI: 10.1093/aje/kwag289
Authors: Samantha J Bents, Soren L Larsen, Sung-mok Jung, Emily Howerton, Lucie Contamin, Sara L Loo, Nicolò Gozzi, Jessica T Davis, Alessandro Vespignani, Matteo Chinazzi, Nicola Perra, Shraddha Ramdas Bandekar, Kaiming Bi, Anass Bouchnita, Spencer J Fox, Lauren Ancel Meyers, Ajitesh Srivastava, Majd Al Aawar, Jiangzhuo Chen, Srinivasan Venkatramanan, Stefan Hoops, Bryan Lewis, Parantapa Bhattacharya, Dustin Machi, Madhav Marathe, Sen Pei, Q Yao, Qiqi Yang, Alison Hill, Anjalika Nande, Joseph Lemaitre, Timothy Willard, Carl A B Pearson, Emily Przykucki, Clifton McKee, Joshua Macdonald, Tijs Alleman, Claire P Smith, Erica Carcelen, Abas Shkembi, Katie Yan, Harry Hochheiser, Cecile Viboud, Justin Lessler, Katriona Shea, Tomás M León, Shaun Truelove
Institutions: National Institutes of Health, Stanford Medicine, Fogarty International Center, Stanford University, University of Illinois Urbana-Champaign, University of California, Berkeley, National University of Singapore, Princeton University, University of Pittsburgh, Johns Hopkins University, Northeastern University, Institute for Scientific Interchange, Queen Mary University of London, The University of Texas at Austin, The University of Texas Health Science Center at Houston, The University of Texas at El Paso, University of Georgia, University of Southern California, University of Virginia, Columbia University, University of North Carolina at Chapel Hill, University of Michigan, Pennsylvania State University, California Department of Public Health
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,856
OpenAlex ID: W7225805483
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