Separating signal from noise in wastewater data: An algorithm to identify community-level COVID-19 surges in real time.

ABSTRACT: 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

SUBMITTER: Keshaviah A 

PROVIDER: S-EPMC10401018 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature