Multi-model insights into racial and ethnic heterogeneities in early COVID-19 transmission and severity in the U.S.

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.

Authors

Institutions

Publication Details

Journal
American Journal of Epidemiology
Published
2026-11-11
DOI
https://doi.org/10.1093/aje/kwag289
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Multi-model insights into racial and ethnic heterogeneities in early COVID-19 transmission and severity in the U.S.

Ajitesh Srivastava, Dustin Machi, Clifton McKee, Anass Bouchnita et al.
American Journal of Epidemiology
COVID-19 epidemiological studies
article

Multi-model insights into racial and ethnic heterogeneities in early COVID-19 transmission and severity in the U.S.

Ajitesh Srivastava, Dustin Machi, Clifton McKee, Anass Bouchnita, Sung-mok Jung, MADHAV V. MARATHE, Rajib Paul, Carl A. B. Pearson, Tomás M. León, Kaiming Bi, Samantha J. Bents, Nicola Perra, Anjalika Nande, C S Smith, Katriona Shea, Justin Lessler, Jessica T. Davis, Alessandro Vespignani, Sara L. Loo, Emily Howerton, Abas Shkembi, Shraddha Ramdas Bandekar, Srinivasan Venkatramanan, Spencer J. Fox, Joseph Chadi Lemaitre, Matteo Chinazzi, Jiangzhuo Chen, Majd Al Aawar, Nicolò Gozzi, Bryan Leroy Lewis, Cecile G. Viboud, Katie Yan, Stefan Hoops, Lauren W. Ancel, Lucie Contamin, Harry Hochheiser, Timothy Willard, Shaun Truelove, Sen Pei, Erica C. Carcelén, Emily Przykucki, Qiqi Yang, Joshua Macdonald, Soren L. Larsen, Alison Hill, Q Yao, Tijs Alleman
article en

Abstract

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.

American Journal of Epidemiology
University of North Carolina at Chapel Hill (US), University of Southern California (US), Northeastern University (US), National Institutes of Health (US), Pennsylvania State University (US), California Department of Public Health (US), Institute for Scientific Interchange (IT), Johns Hopkins University (US), University of Illinois Urbana-Champaign (US), The University of Texas at El Paso (US), University of Georgia (US), National University of Singapore (SG), Queen Mary University of London (GB), University of Pittsburgh (US), Princeton University (US), University of Michigan (US), Stanford Medicine (US), University of Virginia (US), Columbia University (US), The University of Texas at Austin (US), Fogarty International Center (US), The University of Texas Health Science Center at Houston (US), University of California, Berkeley (US), Stanford University (US)
COVID-19 epidemiological studies
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon