Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States
- 20 January 2023
- journal article
- research article
- Published by American Association for the Advancement of Science (AAAS) in Science Advances
- Vol. 9 (3), eabq0199
- https://doi.org/10.1126/sciadv.abq0199
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 Rt becomes larger than 1 for a period of 2 weeks.This publication has 71 references indexed in Scilit:
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