From Wastewater to Infection Estimates: Incident COVID-19 Infections during Omicron in the U.S.

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Abstract
Reconstructing the course of the COVID-19 pandemic through infection estimates is important for assessing disease burden and characterizing transmission dynamics. While wastewater concentration data have been used to estimate infections in localized pre-Omicron studies, a scalable approach that incorporates reinfections and variant-specific shedding remains underdeveloped. To this end, we develop a multi-source approach to retrospectively estimate daily COVID-19 infections in U.S. states during the Omicron era. Our approach integrates wastewater and seroprevalence surveillance data and further incorporates state-specific reinfections to improve infection estimates during the Delta-Omicron transition period. These refined estimates, along with wastewater concentration data adjusted for limited coverage, are used to calculate variant-specific shedding rates, which inform daily infection estimates going forward. While case-based estimates tend to exhibit striking volatility, these infection estimates show more stable and interpretable patterns that closely align with Omicron subvariant transitions. Moreover, we directly quantify the degree of underreporting, showing the extent that reported cases significantly underestimate disease burden, with the lowest reporting rates of 9.72% in Washington, 9.73% in Minnesota, and 10.70% in New York. In the states under study, case reports capture less than a quarter of total infections, leaving the vast majority unaccounted for in official reports. Furthermore, reporting rates differ markedly across states, with disparities growing over time, reflecting the overall rise in underreporting and location-specific limitations to surveillance accuracy. Finally, we estimate time-varying effective reproduction numbers and growth rates to provide a more accurate and timely picture of transmission dynamics over the Omicron era in U.S. states.

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