An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time.
Journal
Science advances
ISSN: 2375-2548
Titre abrégé: Sci Adv
Pays: United States
ID NLM: 101653440
Informations de publication
Date de publication:
03 2021
03 2021
Historique:
received:
07
07
2020
accepted:
19
01
2021
entrez:
6
3
2021
pubmed:
7
3
2021
medline:
19
3
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 33674304
pii: 7/10/eabd6989
doi: 10.1126/sciadv.abd6989
pmc: PMC7935356
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NIGMS NIH HHS
ID : R01 GM130668
Pays : United States
Commentaires et corrections
Type : UpdateOf
Informations de copyright
Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).
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