Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base

Christina M. Astley, Gaurav Tuli, Kimberly A. Mc Cord, Emily L. Cohn, Benjamin Rader, Tanner J. Varrelman, Samantha L. Chiu, Xiaoyi Deng, Kathleen Stewart, Tamer H. Farag, Kristina M. Barkume, Sarah LaRocca, Katherine A. Morris, Frauke Kreuter and John S. Brownstein
Additional contact information
Christina M. Astley: a Division of Endocrinology, Boston Children’s Hospital, Boston, MA 02115;; b Computational Epidemiology Lab, Boston Children’s Hospital, Boston, MA 02115;; c Harvard Medical School, Boston, MA 02115;; d Broad Institute of Harvard and MIT, Cambridge, MA 02142;
Gaurav Tuli: b Computational Epidemiology Lab, Boston Children’s Hospital, Boston, MA 02115;
Kimberly A. Mc Cord: b Computational Epidemiology Lab, Boston Children’s Hospital, Boston, MA 02115;
Emily L. Cohn: b Computational Epidemiology Lab, Boston Children’s Hospital, Boston, MA 02115;
Benjamin Rader: b Computational Epidemiology Lab, Boston Children’s Hospital, Boston, MA 02115;; e Department of Epidemiology, Boston University, Boston, MA 02118;
Tanner J. Varrelman: b Computational Epidemiology Lab, Boston Children’s Hospital, Boston, MA 02115;
Samantha L. Chiu: f Joint Program in Survey Methodology, University of Maryland, College Park, MD 20742;
Xiaoyi Deng: f Joint Program in Survey Methodology, University of Maryland, College Park, MD 20742;
Kathleen Stewart: g Center for Geospatial Information Science, University of Maryland, College Park, MD 20742;
Tamer H. Farag: h Meta, Menlo Park, CA 94025;
Kristina M. Barkume: h Meta, Menlo Park, CA 94025;
Sarah LaRocca: h Meta, Menlo Park, CA 94025;
Katherine A. Morris: h Meta, Menlo Park, CA 94025;
Frauke Kreuter: f Joint Program in Survey Methodology, University of Maryland, College Park, MD 20742;; i Department of Statistics, Ludwig-Maximilians-Universität, Munich 80539, Germany
John S. Brownstein: b Computational Epidemiology Lab, Boston Children’s Hospital, Boston, MA 02115;; c Harvard Medical School, Boston, MA 02115;

Proceedings of the National Academy of Sciences, 2021, vol. 118, issue 51, e2111455118

Abstract: The University of Maryland Global COVID Trends and Impact Survey (UMD-CTIS), launched April 2020, is the largest remote global health monitoring system. This study includes ∼30 million responses through December 2020 from all 114 countries/territories with survey weights to adjust for nonresponse and demographics. Using self-reported cross-sectional survey data sampled daily from Facebook users, we confirm consistent demographics and COVID-19 symptoms. Our global model predicts local COVID-19 case trends. Importantly, one survey item strongly correlates with reported cases, demonstrating potential utility in locales with scant UMD-CTIS sampling or government data. Despite limitations resulting from sampling, nonresponse, coverage, and measurement error, UMD-CTIS has the potential to support existing monitoring systems for COVID-19 as well as other new as-yet-undefined global health threats.

Keywords: COVID-19 surveillance; global health; human social sensing; SARS-CoV-2 testing (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.pnas.org/content/118/51/e2111455118.full (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:118:y:2021:p:e2111455118

Access Statistics for this article

More articles in Proceedings of the National Academy of Sciences from Proceedings of the National Academy of Sciences
Bibliographic data for series maintained by PNAS Product Team ().