The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination

Joshua A. Salomon, Alex Reinhart, Alyssa Bilinski, Eu Jing Chua, Wichada La Motte-Kerr, Minttu M. Rönn, Marissa B. Reitsma, Katherine A. Morris, Sarah LaRocca, Tamer H. Farag, Frauke Kreuter, Roni Rosenfeld and Ryan J. Tibshirani
Additional contact information
Joshua A. Salomon: a Department of Health Policy, Stanford University, Stanford, CA 94305;; b Freeman Spogli Institute for International Studies, Stanford University, Stanford, CA 94305;
Alex Reinhart: c Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213;; d Delphi Group, Carnegie Mellon University, Pittsburgh, PA 15213;
Alyssa Bilinski: e Department of Health Services, Policy and Practice, Brown School of Public Health, Providence, RI 02903;
Eu Jing Chua: d Delphi Group, Carnegie Mellon University, Pittsburgh, PA 15213;; f Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213;
Wichada La Motte-Kerr: d Delphi Group, Carnegie Mellon University, Pittsburgh, PA 15213;; f Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213;
Minttu M. Rönn: g Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA 02115;
Marissa B. Reitsma: a Department of Health Policy, Stanford University, Stanford, CA 94305;
Katherine A. Morris: h Demography and Survey Science, Meta, Menlo Park, CA 94025;
Sarah LaRocca: h Demography and Survey Science, Meta, Menlo Park, CA 94025;
Tamer H. Farag: i Health Partnerships, Meta, Menlo Park, CA 94025;
Frauke Kreuter: j Joint Program in Survey Methodology, University of Maryland, College Park, MD 20742;; k Department of Statistics, Ludwig-Maximilians-Universität Munich, Munich 80539, Germany
Roni Rosenfeld: d Delphi Group, Carnegie Mellon University, Pittsburgh, PA 15213;; f Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213;
Ryan J. Tibshirani: c Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213;; d Delphi Group, Carnegie Mellon University, Pittsburgh, PA 15213;; f Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213;

Proceedings of the National Academy of Sciences, 2021, vol. 118, issue 51, e2111454118

Abstract: The US COVID-19 Trends and Impact Survey (CTIS) has operated continuously since April 6, 2020, collecting over 20 million responses. As the largest public health survey conducted in the United States to date, CTIS was designed to facilitate detailed demographic and geographic analyses, track trends over time, and accommodate rapid revision to address emerging priorities. Using examples of CTIS results illuminating trends in symptoms, risks, mitigating behaviors, testing, and vaccination in relation to evolving high-priority policy questions over 12 mo of the pandemic, we illustrate the value of online surveys for tracking patterns and trends in COVID outcomes as an adjunct to official reporting, and showcase unique insights that would not be visible through traditional public health reporting.

Keywords: COVID-19; SARS-CoV2; survey (search for similar items in EconPapers)
Date: 2021
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