Evidence map›Paper›PMID 37601171›Full record

ArticleFrontiers in public health2023

Wastewater-based epidemiology: the crucial role of viral shedding dynamics in small communities.

Marc-Denis Rioux, François Guillemette, Karine Lemarchand, Kim Doiron, Jean-François Lemay, Thomas Maere, Patrick Dolcé, Patrik Quessy, Nanouk Abonnenc, Peter A Vanrolleghem and 1 more

Abstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Marc-Denis RiouxDepartment of Mathematics and Engineering, Université du Québec à Rimouski, Quebec, QC, Canada.
François GuillemetteDepartment of Environmental Science, Université du Québec à Trois-Rivière, Quebec, QC, Canada.
Karine LemarchandInstitut des Sciences de la Mer, Université du Québec à Rimouski, Quebec, QC, Canada.
Kim DoironNorthern Institute for Research in Environment and Occupational Health and Safety, Quebec, QC, Canada.
Jean-François LemayCentre National en Électrochimie et Technologies Environnementales, Cegep of Shawinigan, Quebec, QC, Canada.
Thomas MaeremodelEAU, Département de génie civil et de génie des eaux, Université Laval, Quebec, QC, Canada.
Patrick DolcéCentre Intégré de Santé et de services sociaux du Bas-Saint-Laurent, Quebec, QC, Canada.
Patrik QuessyCentre National en Électrochimie et Technologies Environnementales, Cegep of Shawinigan, Quebec, QC, Canada.
Nanouk AbonnencCentre National en Électrochimie et Technologies Environnementales, Cegep of Shawinigan, Quebec, QC, Canada.
Peter A VanrolleghemmodelEAU, Département de génie civil et de génie des eaux, Université Laval, Quebec, QC, Canada.
Dominic FrigonDepartment of Civil Engineering, McGill University, Quebec, QC, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Wastewater surveillance (WWS) of pathogens is a rapidly evolving field owing to the 2019 coronavirus disease pandemic, which brought about a paradigm shift in public health authorities for the management of pathogen outbreaks. However, the interpretation of WWS in terms of clinical cases remains a challenge, particularly in small communities where large variations in pathogen concentrations are routinely observed without a clear relation to clinical incident cases. Methods: Results are presented for WWS from six municipalities in the eastern part of Canada during the spring of 2021. We developed a numerical model based on viral kinetics reduction functions to consider both prevalent and incident cases to interpret the WWS data in light of the reported clinical cases in the six surveyed communities. Results: The use of the proposed numerical model with a viral kinetics reduction function drastically increased the interpretability of the WWS data in terms of the clinical cases reported for the surveyed community. In line with our working hypothesis, the effects of viral kinetics reduction modeling were more important in small communities than in larger communities. In all but one of the community cases (where it had no effect), the use of the proposed numerical model led to a change from a +1.5% (for the larger urban center, Quebec City) to a +48.8% increase in the case of a smaller community (Drummondville). Conclusion: Consideration of prevalent and incident cases through the proposed numerical model increases the correlation between clinical cases and WWS data. This is particularly the case in small communities. Because the proposed model is based on a biological mechanism, we believe it is an inherent part of any wastewater system and, hence, that it should be used in any WWS analysis where the aim is to relate WWS measurement to clinical cases.

Indexed as

CoronavirusWastewaterCanadaVirus SheddingWastewater-Based Epidemiological MonitoringWastewaterpathogenssmall communitiesviral shedding dynamicswastewater-based epidemiologywastewater surveillance

Identifiers

PMID37601171
PMCPMC10433918

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.