Title:

Nursing Home Staff Networks and COVID-19

Abstract:

Skilled nursing homes (SNFs) accounted for a disproportionate share of COVID-19 fatalities worldwide, with outbreaks persisting despite the March 2020 nationwide ban on visitors. Using device-level geolocation data for 50 million smartphones, we analyze SNF connections via shared staff and observe 500,000 individuals entering at least one SNF, with 5.1% entering two or more facilities. Nursing homes share connections with 7.1 other facilities, on average. Network measures of connectivity, including node degree, strength and Eigenvector centrality, are highly predictive of COVID-19 cases, whereas traditional regulatory quality metrics are unimportant in predicting outbreak size.

Bio:

Elisa Long is an Associate Professor of Decisions, Operations, and Technology Management at UCLA Anderson, and was previously on the faculty at the Yale School of Management. Her research spans topics in healthcare operations, including epidemic control, hospital resource allocation, breast cancer decision-making, and most recently, nursing home staff networks during the COVID pandemic. She teaches courses on Data & Decisions and Healthcare Analytics, and has received several teaching and research awards. She earned a PhD in Management Science & Engineering from Stanford, and a BS in Operations Research from Cornell.