Separating Signal from Noise in Wastewater Data: An Algorithm to Identify Community-Level COVID-19 Surges

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’ COVID-19 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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The analyses were supported using grant funding from Dogwood Health Trust. Wastewater monitoring was supported by the North Carolina Department of Health and Human Services (who received funding from the Centers for Disease Control and Prevention's National Wastewater Surveillance System) and Dogwood Health Trust. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used ONLY aggregate human data from the publicly available NC COVID dashboard, which meets NCDHHS Division of Public Health data suppression guidelines. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced are available online at