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An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time.
Kogan, Nicole E; Clemente, Leonardo; Liautaud, Parker; Kaashoek, Justin; Link, Nicholas B; Nguyen, Andre T; Lu, Fred S; Huybers, Peter; Resch, Bernd; Havas, Clemens; Petutschnig, Andreas; Davis, Jessica; Chinazzi, Matteo; Mustafa, Backtosch; Hanage, William P; Vespignani, Alessandro; Santillana, Mauricio.
  • Kogan NE; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA. msantill@fas.harvard.edu nkogan@g.harvard.edu leonardo.clemente@childrens.harvard.edu parker_liautaud@g.harvard.edu.
  • Clemente L; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Liautaud P; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA. msantill@fas.harvard.edu nkogan@g.harvard.edu leonardo.clemente@childrens.harvard.edu parker_liautaud@g.harvard.edu.
  • Kaashoek J; Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA. msantill@fas.harvard.edu nkogan@g.harvard.edu leonardo.clemente@childrens.harvard.edu parker_liautaud@g.harvard.edu.
  • Link NB; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
  • Nguyen AT; School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
  • Lu FS; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
  • Huybers P; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Resch B; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
  • Havas C; University of Maryland, Baltimore County, Baltimore, MD, USA.
  • Petutschnig A; Booz Allen Hamilton, Columbia, MD, USA.
  • Davis J; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
  • Chinazzi M; Department of Statistics, Stanford University, Stanford, CA, USA.
  • Mustafa B; Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA.
  • Hanage WP; School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
  • Vespignani A; Department of Geoinformatics - Z_GIS, University of Salzburg, Salzburg, Austria.
  • Santillana M; Center for Geographic Analysis, Harvard University, Cambridge, MA, USA.
Sci Adv ; 7(10)2021 03.
Article in English | MEDLINE | ID: covidwho-1119270
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Epidemiological Monitoring / SARS-CoV-2 / COVID-19 Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Topics: Vaccines Limits: Humans Country/Region as subject: North America Language: English Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Epidemiological Monitoring / SARS-CoV-2 / COVID-19 Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Topics: Vaccines Limits: Humans Country/Region as subject: North America Language: English Year: 2021 Document Type: Article