As the COVID-19 pandemic emerged in early 2020 and rapidly spread across the US, an urgent need developed to improve our current understanding of what factors increase infection risk, likelihood of severe illness, or poor outcomes. Early reports suggest genetics, personal health history, socioeconomic factors, and one's environment increases risk of infection or differences in outcomes, but little is known with high confidence. As there are currently no vaccinations or other preventative treatment, understanding clinical and genetic risk factors would immediately improve our ability to manage the pandemic across populations and deliver precision care at the bedside. The Electronic Medical Records and Genomics (eMERGE) Network has the expertise and resources to investigate the factors leading to increased COVID disease susceptibility by rapidly compiling data from electronic health records (EHRs) and mining records for gene and disease associations. To perform this task well, the features of COVID disease course and characteristics of patients with COVID-19 and those who serve as controls must be precisely defined (“ePhenotyped”) across different record system. Our experience with phenotyping and imputing genomic and EHR data across large populations will enable us to quickly merge a large number of COVID-19 patients for future genome and phenome wide association studies, polygenic risk assessments, and candidate gene studies. Our specific aims include first to create and deploy ePhenotypes for immediate research use establishing a COVID case definition, severity scale, and comorbidities with relation to outcomes. Secondly, we propose to collect COVID EHR and genomic data centrally for future translational research. These resources will be beneficial to the scientific community, necessary to predict comprehensive risk of disease across the lifespan, and have the potential to impact downstream patient care.