Longitudinal monitoring of SARS-CoV-2 in wastewater using viral genetic markers and the estimation of unconfirmed COVID-19 cases.


Journal

The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500

Informations de publication

Date de publication:
15 Apr 2022
Historique:
received: 14 10 2021
revised: 21 12 2021
accepted: 03 01 2022
pubmed: 13 1 2022
medline: 5 3 2022
entrez: 12 1 2022
Statut: ppublish

Résumé

In this study, wastewater-based surveillance was carried out to establish the correlation between SARS-CoV-2 viral RNA concentrations in wastewater and the incidence of corona virus disease 2019 (COVID-19) from clinical testing. The influent wastewater of three major water reclamation facilities (WRFs) in Northern Nevada, serving a population of 390,750, was monitored for SARS-CoV-2 viral RNA gene markers, N1 and N2, from June 2020 through September 2021. A total of 614 samples were collected and analyzed. The SARS-CoV-2 concentrations in wastewater were observed to peak twice during the study period. A moderate correlation trend between coronavirus disease 2019 (COVID-19) incidence data from clinical testing and SARS-CoV-2 viral RNA concentrations in wastewater was observed (Spearman r = 0.533). This correlation improved when using weekly average SARS-CoV-2 marker concentrations of wastewater and clinical case data (Spearman r = 0.790), presumably by mitigating the inherent variability of the environmental dataset and the effects of clinical testing artifacts (e.g., reporting lags). The research also demonstrated the value of wastewater-based surveillance as an early warning signal for early detection of trends in COVID-19 incidence. This was accomplished by identifying that the reported clinical cases had a stronger correlation to SARS-CoV-2 wastewater monitoring data when they were estimated to lag 7-days behind the wastewater data. The results aided local decision makers in developing strategies to manage COVID-19 in the region and provide a framework for how wastewater-based surveillance can be applied across localities to enhance the public health monitoring of the ongoing pandemic.

Identifiants

pubmed: 35016937
pii: S0048-9697(22)00047-X
doi: 10.1016/j.scitotenv.2022.152958
pmc: PMC8743272
pii:
doi:

Substances chimiques

Genetic Markers 0
RNA, Viral 0
Waste Water 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

152958

Informations de copyright

Copyright © 2022 Elsevier B.V. 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.

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Auteurs

Lin Li (L)

Department of Civil and Environmental Engineering, University of Nevada Reno, Reno, NV 89557, USA.

Lauren Mazurowski (L)

Department of Civil and Environmental Engineering, University of Nevada Reno, Reno, NV 89557, USA.

Aimee Dewan (A)

Department of Civil and Environmental Engineering, University of Nevada Reno, Reno, NV 89557, USA.

Madeline Carine (M)

Department of Civil and Environmental Engineering, University of Nevada Reno, Reno, NV 89557, USA.

Laura Haak (L)

Department of Civil and Environmental Engineering, University of Nevada Reno, Reno, NV 89557, USA.

Tatiana C Guarin (TC)

Department of Civil and Environmental Engineering, University of Nevada Reno, Reno, NV 89557, USA.

Niloufar Gharoon Dastjerdi (NG)

Department of Civil and Environmental Engineering, University of Nevada Reno, Reno, NV 89557, USA.

Daniel Gerrity (D)

Southern Nevada Water Authority, P.O. Box 99954, Las Vegas, NV 89193, USA.

Casey Mentzer (C)

Truckee Meadows Water Reclamation Facility, Sparks, NV 89502, USA.

Krishna R Pagilla (KR)

Department of Civil and Environmental Engineering, University of Nevada Reno, Reno, NV 89557, USA. Electronic address: pagilla@unr.edu.

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Classifications MeSH