Paper Information

Reconstructing the course of the COVID-19 pandemic through estimating incident infections 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 estimates variant-specific shedding rates and that accounts for underreporting 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 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 in a sample of seven U.S. states. In these states, case reports capture less than a quarter of total infections, leaving the vast majority unaccounted for in official reports. 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.