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Background: Wastewater testing offers a cost-effective strategy for measuring population disease prevalence and health behaviors. For COVID-19, wastewater surveillance addresses testing gaps and provides an early warning for outbreaks. As U.S. federal agencies build a National Wastewater Surveillance System around the pandemic, thinking through ways to develop flexible frameworks for wastewater sampling, testing, and reporting can avoid unnecessary system overhauls for future infectious disease, chronic disease, and drug epidemics., Objectives: We discuss ways to transform a historically academic exercise into a tool for epidemic response. We generalize lessons learned by a global network of wastewater researchers around validation and implementation for COVID-19 and opioids while also drawing on our experience with wastewater-based epidemiology in the United States., Discussion: Sustainable wastewater surveillance requires coordination between health and safety officials, utilities, labs, and researchers. Adapting sampling frequency, type, and location to threat level, community vulnerability, biomarker properties, and decisions that wastewater data will inform can increase the practical value of the data. Marketplace instabilities, coupled with a fragmented testing landscape due to specialization, may require officials to engage multiple labs to test for known and unknown threats. Government funding can stabilize the market, balancing commercial pressures with public good, and incentivize data sharing. When reporting results, standardizing metrics and contextualizing wastewater data with health resource data can provide insights into a community's vulnerability and identify strategies to prevent health care systems from being overwhelmed. If wastewater data will inform policy decisions for an entire community, comparing characteristics of the wastewater treatment plant's service population to those of the larger community can help determine whether the wastewater data are generalizable. Ethical protocols may be needed to protect privacy and avoid stigmatization. With data-driven approaches to sample collection, analysis, and interpretation, officials can use wastewater surveillance for adaptive resource allocation, pandemic management, and program evaluation. https://doi.org/10.1289/EHP8572.

Wastewater monitoring has provided health officials with early warnings for new COVID-19 outbreaks, but to date, no approach has been validated to distinguish signal (sustained surges) from noise (background variability) in wastewater data to alert officials to the need for heightened public health response. We analyzed 62 wk of data from 19 sites participating in the North Carolina Wastewater Monitoring Network to characterize wastewater metrics around the Delta and Omicron surges. We found that wastewater data identified outbreaks 4 to 5 d before case data (reported on the earlier of the symptom start date or test collection date), on average. At most sites, correlations between wastewater and case data were similar regardless of how wastewater concentrations were normalized and whether calculated with county-level or sewershed-level cases, suggesting that officials may not need to geospatially align case data with sewershed boundaries to gain insights into disease transmission. Although wastewater trend lines captured clear differences in the Delta versus Omicron surge trajectories, no single wastewater metric (detectability, percent change, or flow-population normalized viral concentrations) reliably signaled when these surges started. After iteratively examining different combinations of these three metrics, we developed the Covid-SURGE (Signaling Unprecedented Rises in Groupwide Exposure) algorithm, which identifies unprecedented signals in the wastewater data. With a true positive rate of 82%, a false positive rate of 7%, and strong performance during both surges and in small and large sites, our algorithm provides public health officials with an automated way to flag community-level COVID-19 surges in real time. [ABSTRACT FROM AUTHOR]

Objectives: We sought to determine whether self-reported intent to comply with public health recommendations correlates with future coronavirus disease 2019 (COVID-19) disease burden.Methods: A cross-sectional, online survey of US adults, recruited by snowball sampling, from April 9 to July 12, 2020. Primary measurements were participant survey responses about their intent to comply with public health recommendations. Each participant's intent to comply was compared with his or her local COVID-19 case trajectory, measured as the 7-day rolling median percentage change in COVID-19 confirmed cases within participants' 3-digit ZIP code area, using public county-level data, 30 days after participants completed the survey.Results: After applying raking techniques, the 10,650-participant sample was representative of US adults with respect to age, sex, race, and ethnicity. Intent to comply varied significantly by state and sex. Lower reported intent to comply was associated with higher COVID-19 case increases during the following 30 days. For every 3% increase in intent to comply with public health recommendations, which could be achieved by improving average compliance by a single point for a single item, we estimate a 9% reduction in new COVID-19 cases during the subsequent 30 days.Conclusions: Self-reported intent to comply with public health recommendations may be used to predict COVID-19 disease burden. Measuring compliance intention offers an inexpensive, readily available method of predicting disease burden that can also identify populations most in need of public health education aimed at behavior change. [ABSTRACT FROM AUTHOR]