Campus node-based wastewater surveillance enables COVID-19 case localization and confirms lower SARS-CoV-2 burden relative to the surrounding community.
Dormitories
Epidemiology
Nodal monitoring
Residences
Wastewater treatment plant
Wastewater-based epidemiology
Worksite
Journal
Water research
ISSN: 1879-2448
Titre abrégé: Water Res
Pays: England
ID NLM: 0105072
Informations de publication
Date de publication:
01 Oct 2023
01 Oct 2023
Historique:
received:
18
03
2023
revised:
06
08
2023
accepted:
07
08
2023
medline:
23
10
2023
pubmed:
28
8
2023
entrez:
27
8
2023
Statut:
ppublish
Résumé
Wastewater-based surveillance (WBS) has been established as a powerful tool that can guide health policy at multiple levels of government. However, this approach has not been well assessed at more granular scales, including large work sites such as University campuses. Between August 2021 and April 2022, we explored the occurrence of SARS-CoV-2 RNA in wastewater using qPCR assays from multiple complimentary sewer catchments and residential buildings spanning the University of Calgary's campus and how this compared to levels from the municipal wastewater treatment plant servicing the campus. Real-time contact tracing data was used to evaluate an association between wastewater SARS-CoV-2 burden and clinically confirmed cases and to assess the potential of WBS as a tool for disease monitoring across worksites. Concentrations of wastewater SARS-CoV-2 N1 and N2 RNA varied significantly across six sampling sites - regardless of several normalization strategies - with certain catchments consistently demonstrating values 1-2 orders higher than the others. Relative to clinical cases identified in specific sewersheds, WBS provided one-week leading indicator. Additionally, our comprehensive monitoring strategy enabled an estimation of the total burden of SARS-CoV-2 for the campus per capita, which was significantly lower than the surrounding community (p≤0.001). Allele-specific qPCR assays confirmed that variants across campus were representative of the community at large, and at no time did emerging variants first debut on campus. This study demonstrates how WBS can be efficiently applied to locate hotspots of disease activity at a very granular scale, and predict disease burden across large, complex worksites.
Identifiants
pubmed: 37634459
pii: S0043-1354(23)00909-0
doi: 10.1016/j.watres.2023.120469
pii:
doi:
Substances chimiques
Wastewater
0
RNA, Viral
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
120469Informations de copyright
Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.