Wastewater monitoring has shown promise in providing an early warning for new COVID-19 outbreaks, but to date, no approach has been validated to reliably distinguish signal from noise in wastewater data and thereby alert officials to when the data show a need for heightened public health response. We analyzed 62 weeks of data from 19 sites participating in the North Carolina Wastewater Monitoring Network to characterize wastewater metrics before and around the Delta and Omicron surges. We found that, on average, wastewater data identified new outbreaks four to five days before case data (reported based on the earlier of the symptom start date or test collection date). At most sites, correlations between wastewater and case data were similar regardless of how wastewater concentrations were normalized, and correlations were slightly stronger with county-level cases than sewershed-level cases, suggesting that officials may not need to geospatially align case data with sewershed boundaries to gain insights into disease transmission. Wastewater trend lines showed clear differences in the Delta versus Omicron surge trajectories, but no single wastewater metric (detectability, percent change, or flow-population normalized viral concentrations) adequately indicated when these surges started. After iteratively examining different combinations of these three metrics, we developed a simple algorithm that identifies unprecedented signals in the wastewater to help clarify changes in communities' burden. Our novel algorithm accurately identified the start of both the Delta and Omicron surges in 84% of sites, potentially providing public health officials with an automated way to flag community-level COVID-19 surges.

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.