Wastewater-based epidemiology for COVID-19 surveillance and beyond: A survey.

COVID-19 Epidemiology Infectious disease Survey Wastewater

Journal

Epidemics
ISSN: 1878-0067
Titre abrégé: Epidemics
Pays: Netherlands
ID NLM: 101484711

Informations de publication

Date de publication:
26 Sep 2024
Historique:
received: 19 03 2024
revised: 11 09 2024
accepted: 11 09 2024
medline: 3 10 2024
pubmed: 3 10 2024
entrez: 2 10 2024
Statut: aheadofprint

Résumé

The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding wastewater-based epidemiology for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.

Identifiants

pubmed: 39357172
pii: S1755-4365(24)00054-9
doi: 10.1016/j.epidem.2024.100793
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

100793

Informations de copyright

Copyright © 2024 The Authors. Published by 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 of personal relationships that could have appeared to influence the work reported in this article.

Auteurs

Chen Chen (C)

Department of Computer Science, University of Virginia, Charlottesville, 22904, United States. Electronic address: zrh6du@virginia.edu.

Yunfan Wang (Y)

Department of Computer Science, University of Virginia, Charlottesville, 22904, United States. Electronic address: abe6fq@virginia.edu.

Gursharn Kaur (G)

Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States. Electronic address: fug3aj@virginia.edu.

Aniruddha Adiga (A)

Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States. Electronic address: aa5dw@virginia.edu.

Baltazar Espinoza (B)

Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States. Electronic address: be8dq@virginia.edu.

Srinivasan Venkatramanan (S)

Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States. Electronic address: sv8nv@virginia.edu.

Andrew Warren (A)

Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States. Electronic address: sasw3xp@virginia.edu.

Bryan Lewis (B)

Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States. Electronic address: bl4zc@virginia.edu.

Justin Crow (J)

Virginia Department of Health, Richmond, 23219, United States. Electronic address: justin.crow@vdh.virginia.gov.

Rekha Singh (R)

Virginia Department of Health, Richmond, 23219, United States. Electronic address: rekha.singh@vdh.virginia.gov.

Alexandra Lorentz (A)

Division of Consolidated Laboratory Services, Department of General Services, Richmond, 23219, United States. Electronic address: alexandra.lorentz@dgs.virginia.gov.

Denise Toney (D)

Division of Consolidated Laboratory Services, Department of General Services, Richmond, 23219, United States. Electronic address: denise.toney@dgs.virginia.gov.

Madhav Marathe (M)

Department of Computer Science, University of Virginia, Charlottesville, 22904, United States; Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States. Electronic address: marathe@virginia.edu.

Classifications MeSH