Tracking COVID-19 Infections Using Survey Data on Rapid At-Home Tests.

Importance: Identifying and tracking new infections during an emerging pandemic is crucial to design and deploy interventions to protect populations and mitigate the pandemic's effects, yet it remains a challenging task. Objective: To characterize the ability of nonprobability online surveys to longitudinally estimate the number of COVID-19 infections in the population both in the presence and absence of institutionalized testing. Design, Setting, and Participants: Internet-based online nonprobability surveys were conducted among residents aged 18 years or older across 50 US states and the District of Columbia, using the PureSpectrum survey vendor, approximately every 6 weeks between June 1, 2020, and January 31, 2023, for a multiuniversity consortium-the COVID States Project. Surveys collected information on COVID-19 infections with representative state-level quotas applied to balance age, sex, race and ethnicity, and geographic distribution. Main Outcomes and Measures: The main outcomes were (1) survey-weighted estimates of new monthly confirmed COVID-19 cases in the US from January 2020 to January 2023 and (2) estimates of uncounted test-confirmed cases from February 1, 2022, to January 1, 2023. These estimates were compared with institutionally reported COVID-19 infections collected by Johns Hopkins University and wastewater viral concentrations for SARS-CoV-2 from Biobot Analytics. Results: The survey spanned 17 waves deployed from June 1, 2020, to January 31, 2023, with a total of 408 515 responses from 306 799 respondents (mean [SD] age, 42.8 [13.0] years; 202 416 women [66.0%]). Overall, 64 946 respondents (15.9%) self-reported a test-confirmed COVID-19 infection. National survey-weighted test-confirmed COVID-19 estimates were strongly correlated with institutionally reported COVID-19 infections (Pearson correlation, r = 0.96; P < .001) from April 2020 to January 2022 (50-state correlation mean [SD] value, r = 0.88 [0.07]). This was before the government-led mass distribution of at-home rapid tests. After January 2022, correlation was diminished and no longer statistically significant (r = 0.55; P = .08; 50-state correlation mean [SD] value, r = 0.48 [0.23]). In contrast, survey COVID-19 estimates correlated highly with SARS-CoV-2 viral concentrations in wastewater both before (r = 0.92; P < .001) and after (r = 0.89; P < .001) January 2022. Institutionally reported COVID-19 cases correlated (r = 0.79; P < .001) with wastewater viral concentrations before January 2022, but poorly (r = 0.31; P = .35) after, suggesting that both survey and wastewater estimates may have better captured test-confirmed COVID-19 infections after January 2022. Consistent correlation patterns were observed at the state level. Based on national-level survey estimates, approximately 54 million COVID-19 cases were likely unaccounted for in official records between January 2022 and January 2023. Conclusions and Relevance: This study suggests that nonprobability survey data can be used to estimate the temporal evolution of test-confirmed infections during an emerging disease outbreak. Self-reporting tools may enable government and health care officials to implement accessible and affordable at-home testing for efficient infection monitoring in the future.