ABSTRACT
The growing toll of the COVID-19 pandemic has heightened the urgency of identifying individuals most at risk of infection and severe outcomes, underscoring the need to assess susceptibility and severity patterns in large datasets. The AncestryDNA COVID-19 Study collected self-reported survey data on symptoms, outcomes, risk factors, and exposures for over 563,000 adult individuals in the U.S., including over 4,700 COVID-19 cases as measured by a self-reported positive nasal swab test. We observed significant associations between several risk factors and COVID-19 susceptibility and severity outcomes. Many of the susceptibility associations were accounted for by differences in known exposures; a notable exception was elevated susceptibility odds for males after adjusting for known exposures and age. We also leveraged the dataset to build risk models to robustly predict individualized COVID-19 susceptibility (area under the curve [AUC]=0.84) and severity outcomes including hospitalization and life-threatening critical illness amongst COVID-19 cases (AUC=0.87 and 0.90, respectively). The results highlight the value of self-reported epidemiological data at scale to provide public health insights into the evolving COVID-19 pandemic.