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

Author

Listed:
  • 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;)

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.

Suggested Citation

  • 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. Barkum, 2021. "Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(51), pages 2111455118-, December.
    • Handle: RePEc:nas:journl:v:118:y:2021:p:e2111455118
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    Cited by:

    1. Susan Athey & Kristen Grabarz & Michael Luca & Nils Wernerfelt, 2023. "Digital public health interventions at scale: The impact of social media advertising on beliefs and outcomes related to COVID vaccines," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 120(5), pages 2208110120-, January.

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