Separating Signal from Noise in Wastewater Data: An Algorithm to Identify Community-Level COVID-19 Surges in Real Time
The Rockefeller Foundation
- Trends in wastewater data from the North Carolina Wastewater Monitoring Network captured the differing trajectories of the Delta and Omicron surges.
- No single wastewater metric—detectability, percent change, or magnitude of normalized viral concentrations —reliably signaled the start of the Delta or Omicron surge across the 19 sites analyzed.
- To reliably distinguish signal (sustained surges) from noise (background variability) in wastewater data, officials should combine multiple wastewater metrics to characterize COVID-19 dynamics.
- The Covid-SURGE algorithm successfully identified the start of the Delta and Omicron surges, with a true positive rate of 82 percent false positive rate of 7 percent, and strong performance in sites with small and large wastewater treatment plants.
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 weeks 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 days 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 percent, a false positive rate of 7 percent, 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.