Using Opt-in Non-probability Surveys to Estimate COVID-19 Infection and Vaccination Rates

Authors

• Alexi Quintana-Mathe, Northeastern University
• Ata Uslu, Northeastern University
• Jason Radford, Northeastern University
• James Druckman, University of Rochester
• Kristin Lunz Trujillo, Boston College
• Alauna Safarpour, Gettysburg CollegeFollow
• Katherine Ognyanova, Rutgers University
• Matthew Baum, Harvard University
• Jonathan Schulman, Pew Research Center
• Roy Perlis, Massachusetts General Hospital
• Mauricio Santillana, Harvard Medical School
• David Lazer, Harvard University

Document Type

Article

Publication Date

7-20-2026

Department 1

Political Science

Abstract

Public health crises demand timely surveillance of disease and interventions. This was a substantial challenge during COVID-19 in the United States due to the federalized response. States varied widely in infection and vaccination rates. Continuous state-level probability samples for tracking were unavailable and would have been logistically and financially onerous. Administrative data eventually became inaccurate. Thus, the authors evaluate the accuracy of large-scale opt-in non-probability state and national samples (from the Covid States Project) that estimated infection and vaccination rates on a near-continuous basis. The estimates aligned closely with a high-quality national probability sample and state-level administrative data (when such data were reliable). The authors offer evidence that the success of the surveys, compared to other less accurate non-probability surveys, may have stemmed from the successful recruitment of respondents with low trust in health institutions (i.e., individuals who often avoid surveys). They conclude by discussing how the Covid States Project can inform further data collection efforts requiring extensive spatial and temporal coverage.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

DOI

10.1093/jssam/smaf041

Version

Version of Record

Recommended Citation

Quintana-Mathe,Alexi, Ata Aydin Uslu, Jason Radford, James N. Druckman, Kristin Lunz Trujillo, Alauna Safarpour, Katherine Ognyanova, Matthew A. Baum, Jonathan Schulman, Roy H. Perlis, Mauricio Santillana, and David Lazer. “Using Opt-in Non-Probability Surveys to Estimate COVID-19 Infection and Vaccination Rates.” Journal of Survey Statistics and Methodology 14, no. 3 (2026): 887-910.