Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States
Lucas M. Stolerman, Leonardo Clemente, Canelle Poirier, Kris V. Parag, Atreyee Majumder, Serge Masyn, Bernd Resch, Mauricio Santillana
Abstract
Coronavirus disease 2019 (COVID-19) continues to affect the world, and the design of strategies to curb disease outbreaks requires close monitoring of their trajectories. We present machine learning methods that leverage internet-based digital traces to anticipate sharp increases in COVID-19 activity in U.S. counties. In a complementary direction to the efforts led by the Centers for Disease Control and Prevention (CDC), our models are designed to detect the time when an uptrend in COVID-19 activity will occur. Motivated by the need for finer spatial resolution epidemiological insights, we build upon previous efforts conceived at the state level. Our methods—tested in an out-of-sample manner, as events were unfolding, in 97 counties representative of multiple population sizes across the United States—frequently anticipated increases in COVID-19 activity 1 to 6 weeks before local outbreaks, defined when the effective reproduction number R t becomes larger than 1 for a period of 2 weeks.