tetano
Editor, Senior Moderator
Sci Adv
. 2021 Mar 5;7(10):eabd6989.
doi: 10.1126/sciadv.abd6989. Print 2021 Mar.
An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time
Nicole E Kogan[SUP] 1 2 [/SUP], Leonardo Clemente[SUP] 1 [/SUP], Parker Liautaud[SUP] 3 [/SUP], Justin Kaashoek[SUP] 4 5 [/SUP], Nicholas B Link[SUP] 4 6 [/SUP], Andre T Nguyen[SUP] 4 7 8 [/SUP], Fred S Lu[SUP] 4 9 [/SUP], Peter Huybers[SUP] 10 5 [/SUP], Bernd Resch[SUP] 11 12 [/SUP], Clemens Havas[SUP] 11 [/SUP], Andreas Petutschnig[SUP] 11 [/SUP], Jessica Davis[SUP] 13 [/SUP], Matteo Chinazzi[SUP] 13 [/SUP], Backtosch Mustafa[SUP] 4 14 [/SUP], William P Hanage[SUP] 2 [/SUP], Alessandro Vespignani[SUP] 13 [/SUP], Mauricio Santillana[SUP] 1 2 5 15 [/SUP]
Affiliations
Abstract
Given still-high levels of coronavirus disease 2019 (COVID-19) susceptibility and inconsistent transmission-containing strategies, outbreaks have continued to emerge across the United States. Until effective vaccines are widely deployed, curbing COVID-19 will require carefully timed nonpharmaceutical interventions (NPIs). A COVID-19 early warning system is vital for this. Here, we evaluate digital data streams as early indicators of state-level COVID-19 activity from 1 March to 30 September 2020. We observe that increases in digital data stream activity anticipate increases in confirmed cases and deaths by 2 to 3 weeks. Confirmed cases and deaths also decrease 2 to 4 weeks after NPI implementation, as measured by anonymized, phone-derived human mobility data. We propose a means of harmonizing these data streams to identify future COVID-19 outbreaks. Our results suggest that combining disparate health and behavioral data may help identify disease activity changes weeks before observation using traditional epidemiological monitoring.
. 2021 Mar 5;7(10):eabd6989.
doi: 10.1126/sciadv.abd6989. Print 2021 Mar.
An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time
Nicole E Kogan[SUP] 1 2 [/SUP], Leonardo Clemente[SUP] 1 [/SUP], Parker Liautaud[SUP] 3 [/SUP], Justin Kaashoek[SUP] 4 5 [/SUP], Nicholas B Link[SUP] 4 6 [/SUP], Andre T Nguyen[SUP] 4 7 8 [/SUP], Fred S Lu[SUP] 4 9 [/SUP], Peter Huybers[SUP] 10 5 [/SUP], Bernd Resch[SUP] 11 12 [/SUP], Clemens Havas[SUP] 11 [/SUP], Andreas Petutschnig[SUP] 11 [/SUP], Jessica Davis[SUP] 13 [/SUP], Matteo Chinazzi[SUP] 13 [/SUP], Backtosch Mustafa[SUP] 4 14 [/SUP], William P Hanage[SUP] 2 [/SUP], Alessandro Vespignani[SUP] 13 [/SUP], Mauricio Santillana[SUP] 1 2 5 15 [/SUP]
Affiliations
- PMID: 33674304
- DOI: 10.1126/sciadv.abd6989
Abstract
Given still-high levels of coronavirus disease 2019 (COVID-19) susceptibility and inconsistent transmission-containing strategies, outbreaks have continued to emerge across the United States. Until effective vaccines are widely deployed, curbing COVID-19 will require carefully timed nonpharmaceutical interventions (NPIs). A COVID-19 early warning system is vital for this. Here, we evaluate digital data streams as early indicators of state-level COVID-19 activity from 1 March to 30 September 2020. We observe that increases in digital data stream activity anticipate increases in confirmed cases and deaths by 2 to 3 weeks. Confirmed cases and deaths also decrease 2 to 4 weeks after NPI implementation, as measured by anonymized, phone-derived human mobility data. We propose a means of harmonizing these data streams to identify future COVID-19 outbreaks. Our results suggest that combining disparate health and behavioral data may help identify disease activity changes weeks before observation using traditional epidemiological monitoring.