Survey data yields improved estimates of test-confirmed COVID-19 cases when rapid at-home tests were massively distributed in the United States

Importance Identifying and tracking new infections during an emerging pandemic is crucial to design and deploy interventions to protect populations and mitigate its effects, yet it remains a challenging task.

Objective To characterize the ability of non-probability online surveys to longitudinally estimate the number of COVID-19 infections in the population both in the presence and absence of institutionalized testing.

Design Internet-based non-probability surveys were conducted, using the PureSpectrum survey vendor, approximately every 6 weeks between April 2020 and January 2023. They collected information on COVID-19 infections with representative state-level quotas applied to balance age, gender, race and ethnicity, and geographic distribution. Data from this survey were compared to institutional case counts collected by Johns Hopkins University and wastewater surveillance data for SARS-CoV-2 from Biobot Analytics.

Setting Population-based online non-probability survey conducted for a multi-university consortium —the Covid States Project.

Participants Residents of age 18+ across 50 US states and the District of Columbia in the US.

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