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

Area of Science:

• Epidemiology
• Public Health Surveillance
• Infectious Disease Modeling

Background:

• Accurate tracking of infectious diseases during pandemics is critical for effective public health response.
• Traditional surveillance methods can be challenged by testing accessibility and reporting delays.

Purpose of the Study:

• To assess the utility of nonprobability online surveys for estimating COVID-19 infections over time.
• To compare survey-based infection estimates with official case counts and wastewater surveillance data.

Main Methods:

• Internet-based nonprobability surveys were conducted across the US from June 2020 to January 2023.
• Surveys collected self-reported COVID-19 infection data, with state-level quotas for demographic balancing.
• Estimates were compared with Johns Hopkins University case data and Biobot Analytics wastewater data.

Main Results:

• Survey-estimated COVID-19 cases strongly correlated with official reports until January 2022.
• After January 2022, survey estimates maintained high correlation with wastewater SARS-CoV-2 concentrations, unlike official reports.
• An estimated 54 million COVID-19 cases were potentially unaccounted for in official records between January 2022 and January 2023.

Conclusions:

• Nonprobability survey data provide a valuable tool for estimating infection trends during emerging pandemics.
• Self-reporting surveys, potentially integrated with at-home testing, can enhance future infection monitoring systems.
• This approach offers an accessible and affordable method for real-time public health surveillance.