Emerging
Practices that show potential to achieve desirable public health outcomes in a specific real-life setting and produce early results that are consistent with the objectives of the activities and thus indicate effectiveness.

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

Sun, C. L. F., Zuccarelli, E., Zerhouni, E. G. A., Lee, J., Muller, J., Scott, K.M., Lujan, A.M., Levi, R.

Release Date:

Peer Review Study

Data Collection and Analysis
Healthcare Access and Quality
Tools Included
Outside U.S.
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Data Collection and Reporting

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

Outcomes of Interest

Improve Data Infrastructure

Priority Population(s)

People Living in Congregate Housing

Setting(s) of Implementation

Geographic Area of Implementation

Implementation Period

2020