An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time

Author:

Kogan Nicole E.12ORCID,Clemente Leonardo1ORCID,Liautaud Parker3ORCID,Kaashoek Justin14ORCID,Link Nicholas B.15ORCID,Nguyen Andre T.167ORCID,Lu Fred S.18ORCID,Huybers Peter34ORCID,Resch Bernd910ORCID,Havas Clemens9ORCID,Petutschnig Andreas9ORCID,Davis Jessica11ORCID,Chinazzi Matteo11ORCID,Mustafa Backtosch112,Hanage William P.2ORCID,Vespignani Alessandro11ORCID,Santillana Mauricio12413ORCID

Affiliation:

1. Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, USA.

2. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

3. Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA.

4. School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.

5. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

6. University of Maryland, Baltimore County, Baltimore, MD, USA.

7. Booz Allen Hamilton, Columbia, MD, USA.

8. Department of Statistics, Stanford University, Stanford, CA, USA.

9. Department of Geoinformatics - Z_GIS, University of Salzburg, Salzburg, Austria.

10. Center for Geographic Analysis, Harvard University, Cambridge, MA, USA.

11. Northeastern University, Boston, MA, USA.

12. University Medical Center Hamburg-Eppendorf, Hamburg, Germany.

13. Department of Pediatrics, Harvard Medical School, Boston, MA, USA.

Abstract

Multiple digital data streams forecast COVID-19 activity weeks before traditional epidemiological surveillance.

Funder

National Institute of General Medical Sciences

John P. McGovern Foundation

Chleck Foundation

Google Cloud

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference77 articles.

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