tetano
Editor, Senior Moderator
Sci Adv
. 2023 Jan 18;9(3):eabq0199.
doi: 10.1126/sciadv.abq0199. Epub 2023 Jan 18.
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[SUP] 1 2 3 [/SUP], Leonardo Clemente[SUP] 1 4 [/SUP], Canelle Poirier[SUP] 1 2 [/SUP], Kris V Parag[SUP] 5 [/SUP], Atreyee Majumder[SUP] 6 [/SUP], Serge Masyn[SUP] 6 [/SUP], Bernd Resch[SUP] 7 8 [/SUP], Mauricio Santillana[SUP] 2 4 9 [/SUP]
Affiliations
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[SUB]t[/SUB] becomes larger than 1 for a period of 2 weeks.
. 2023 Jan 18;9(3):eabq0199.
doi: 10.1126/sciadv.abq0199. Epub 2023 Jan 18.
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[SUP] 1 2 3 [/SUP], Leonardo Clemente[SUP] 1 4 [/SUP], Canelle Poirier[SUP] 1 2 [/SUP], Kris V Parag[SUP] 5 [/SUP], Atreyee Majumder[SUP] 6 [/SUP], Serge Masyn[SUP] 6 [/SUP], Bernd Resch[SUP] 7 8 [/SUP], Mauricio Santillana[SUP] 2 4 9 [/SUP]
Affiliations
- PMID: 36652520
- DOI: 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 R[SUB]t[/SUB] becomes larger than 1 for a period of 2 weeks.