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Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base.
Astley, Christina M; Tuli, Gaurav; Mc Cord, Kimberly A; Cohn, Emily L; Rader, Benjamin; Varrelman, Tanner J; Chiu, Samantha L; Deng, Xiaoyi; Stewart, Kathleen; Farag, Tamer H; Barkume, Kristina M; LaRocca, Sarah; Morris, Katherine A; Kreuter, Frauke; Brownstein, John S.
  • Astley CM; Division of Endocrinology, Boston Children's Hospital, Boston, MA 02115; christina.astley@childrens.harvard.edu.
  • Tuli G; Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA 02115.
  • Mc Cord KA; Harvard Medical School, Boston, MA 02115.
  • Cohn EL; Broad Institute of Harvard and MIT, Cambridge, MA 02142.
  • Rader B; Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA 02115.
  • Varrelman TJ; Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA 02115.
  • Chiu SL; Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA 02115.
  • Deng X; Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA 02115.
  • Stewart K; Department of Epidemiology, Boston University, Boston, MA 02118.
  • Farag TH; Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA 02115.
  • Barkume KM; Joint Program in Survey Methodology, University of Maryland, College Park, MD 20742.
  • LaRocca S; Joint Program in Survey Methodology, University of Maryland, College Park, MD 20742.
  • Morris KA; Center for Geospatial Information Science, University of Maryland, College Park, MD 20742.
  • Kreuter F; Meta, Menlo Park, CA 94025.
  • Brownstein JS; Meta, Menlo Park, CA 94025.
Proc Natl Acad Sci U S A ; 118(51)2021 12 21.
Article in English | MEDLINE | ID: covidwho-1569348
ABSTRACT
Simultaneously tracking the global impact of COVID-19 is challenging because of regional variation in resources and reporting. Leveraging self-reported survey outcomes via an existing international social media network has the potential to provide standardized data streams to support monitoring and decision-making worldwide, in real time, and with limited local resources. The University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, has invited daily cross-sectional samples from the social media platform's active users to participate in the survey since its launch on April 23, 2020. We analyzed UMD-CTIS survey data through December 20, 2020, from 31,142,582 responses representing 114 countries/territories weighted for nonresponse and adjusted to basic demographics. We show consistent respondent demographics over time for many countries/territories. Machine Learning models trained on national and pooled global data verified known symptom indicators. COVID-like illness (CLI) signals were correlated with government benchmark data. Importantly, the best benchmarked UMD-CTIS signal uses a single survey item whereby respondents report on CLI in their local community. In regions with strained health infrastructure but active social media users, we show it is possible to define COVID-19 impact trajectories using a remote platform independent of local government resources. This syndromic surveillance public health tool is the largest global health survey to date and, with brief participant engagement, can provide meaningful, timely insights into the global COVID-19 pandemic at a local scale.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Social Media / Public Health Surveillance / COVID-19 Type of study: Diagnostic study / Experimental Studies / Observational study / Randomized controlled trials Limits: Humans Language: English Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Social Media / Public Health Surveillance / COVID-19 Type of study: Diagnostic study / Experimental Studies / Observational study / Randomized controlled trials Limits: Humans Language: English Year: 2021 Document Type: Article