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Inferring SARS-CoV-2 RNA shedding into wastewater relative to time of infection
Sean M. Cavany; Aaron Bivins; Zhenyu Wu; Devin North; Kyle Bibby; Alex Perkins.
Affiliation
  • Sean M. Cavany; University of Notre Dame
  • Aaron Bivins; University of Notre Dame
  • Zhenyu Wu; University of NotreDame
  • Devin North; University of Notre Dame
  • Kyle Bibby; University of Notre Dame
  • Alex Perkins; University of Notre Dame
Preprint in En | PREPRINT-MEDRXIV | ID: ppmedrxiv-21258238
Journal article
A scientific journal published article is available and is probably based on this preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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ABSTRACT
Since the start of the COVID-19 pandemic, there has been interest in using wastewater monitoring as an approach for disease surveillance. A significant uncertainty that would improve interpretation of wastewater monitoring data is the intensity and timing with which individuals shed RNA from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into wastewater. By combining wastewater and case surveillance data sets from a university campus during a period of heightened surveillance, we inferred that individual shedding of RNA into wastewater peaks on average six days (50% uncertainty interval (UI) 6 - 7; 95% UI 4 - 8) following infection, and that wastewater measurements are highly overdispersed (negative binomial dispersion parameter, k = 0.39 (95% credible interval 0.32 - 0.48)). This limits the utility of wastewater surveillance as a leading indicator of secular trends in SARS-CoV-2 transmission during an epidemic, and implies that it could be most useful as an early warning of rising transmission in areas where transmission is low or clinical testing is delayed or of limited capacity.
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Full text: 1 Collection: 09-preprints Database: PREPRINT-MEDRXIV Type of study: Prognostic_studies Language: En Year: 2021 Document type: Preprint
Full text: 1 Collection: 09-preprints Database: PREPRINT-MEDRXIV Type of study: Prognostic_studies Language: En Year: 2021 Document type: Preprint