Your browser doesn't support javascript.
loading
Separating Signal from Noise in Wastewater Data: An Algorithm to Identify Community-Level COVID-19 Surges
Aparna Keshaviah; Ian Huff; Xindi C. Hu; Virginia Guidry; Ariel Christensen; Steven Berkowitz; Stacie Reckling; Rachel T. Noble; Thomas Clerkin; Denene Blackwood; Sandra McLellan; Adelaide Roguet; Isabel Mussa.
Affiliation
  • Aparna Keshaviah; Mathematica
  • Ian Huff; Mathematica
  • Xindi C. Hu; Mathematica
  • Virginia Guidry; North Carolina Department of Health and Human Services, Division of Public Health
  • Ariel Christensen; North Carolina Department of Health and Human Services, Division of Public Health
  • Steven Berkowitz; North Carolina Department of Health and Human Services, Division of Public Health
  • Stacie Reckling; North Carolina Department of Health and Human Services, Division of Public Health
  • Rachel T. Noble; Institute of Marine Sciences, University of North Carolina-Chapel Hill
  • Thomas Clerkin; Institute of Marine Sciences, University of North Carolina-Chapel Hill
  • Denene Blackwood; Institute of Marine Sciences, University of North Carolina-Chapel Hill
  • Sandra McLellan; School of Freshwater Sciences, University of Wisconsin-Milwaukee
  • Adelaide Roguet; School of Freshwater Sciences, University of Wisconsin-Milwaukee
  • Isabel Mussa; Mathematica
Preprint in English | medRxiv | ID: ppmedrxiv-22280095
ABSTRACT
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.
License
cc_no
Full text: Available Collection: Preprints Database: medRxiv Type of study: Prognostic study Language: English Year: 2022 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Prognostic study Language: English Year: 2022 Document type: Preprint
...