Predicting Coronavirus Disease 2019 Infection Risk and Related Risk Drivers in Nursing Homes: A Machine Learning Approach

This is a retrospective cohort study that was used to inform COVID-19 infection prevention measures by identifying and assessing risk and possible vectors of infection in nursing homes (NHs) using a machine-learning approach. The strongest predictors of COVID-19 infection were identified as the county’s infection rate and the number of separate units in the NH; other predictors included the county’s population density, historical health deficiencies, and resident density. In addition, the NH’s historical percentage of non-Hispanic white residents was identified as a protective factor. The study concluded that a machine-learning model can help quantify and predict infection risk.

Resource Details

• Population: People Living in Congregate Housing
• Topics of Practice: Data Collection and Analysis
• Outcomes of Interest: Improve Data Infrastructure
• Level of Evidence: Emerging
• Tools or Materials Included in Resource: No
• Social Determinants of Health: Healthcare Access and Quality
• Outside of US: No