Applying machine learning to identify unrecognized COVID-19 deaths recorded as other causes of death in the United States
Abstract
The actual number of US deaths caused by severe acute respiratory syndrome coronavirus 2 infection has been investigated and debated since the start of the COVID-19 pandemic. Here, we use machine learning trained on US death certificates from March 2020 to December 2021 to predict 155,536 (95% uncertainty interval: 150,062 to 161,112) unrecognized COVID-19 deaths. This indicates that 19% more COVID-19 deaths occurred in the US than officially reported. Predicted unrecognized COVID-19 deaths occurred disproportionately among decedents with less than a high school education; decedents identified as Hispanic, American Indian, Alaska Native, Asian, and/or Black; counties with lower household incomes and worse preexisting health; and counties in the South. These findings suggest that the US death investigation system undercounted COVID-19 deaths unevenly, hiding the true extent of inequities.
Fields of Study & Subjects
COVID-19 epidemiological studies
Insurance
Mortality
Demography
Risk Management
COVID-19 and healthcare impacts
Publication details
- Journal / source
- Science Advances
- Publisher
- American Association for the Advancement of Science (AAAS)
- Volume
- 12
- Issue
- 12
- Pages
- eaef5697–eaef5697
- Publication date
- 18 March 2026
- Language
- EN