Evidence map›Paper›PMID 40796561›Full record

ArticleNature communications2025

Estimated transmission dynamics of SARS-CoV-2 variants from wastewater are unbiased and robust to differential shedding.

David Dreifuss, Jana S Huisman, Johannes C Rusch, Lea Caduff, Pravin Ganesanandamoorthy, Alexander J Devaux, Charles Gan, Tanja Stadler, Tamar Kohn, Christoph Ort and 2 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Observational
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

12 authors.

David DreifussDepartment of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.ORCID http://orcid.org/0000-0002-5827-5387
Jana S HuismanDepartment of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland. jhuisman@mit.edu.ORCID http://orcid.org/0000-0002-1782-8109
Johannes C RuschEawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland.
Lea CaduffEawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland.ORCID http://orcid.org/0000-0003-4485-609X
Pravin GanesanandamoorthyEawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland.
Alexander J DevauxEawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland.
Charles GanEawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland.ORCID http://orcid.org/0000-0001-6554-0954
Tanja StadlerDepartment of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.ORCID http://orcid.org/0000-0001-6431-535X
Tamar KohnLaboratory of Environmental Virology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.ORCID http://orcid.org/0000-0003-0395-6561
Christoph OrtEawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland.ORCID http://orcid.org/0000-0001-9950-2286
Niko BeerenwinkelDepartment of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland. niko.beerenwinkel@bsse.ethz.ch.ORCID http://orcid.org/0000-0002-0573-6119
Timothy R JulianEawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland. tim.julian@eawag.ch.ORCID http://orcid.org/0000-0003-1000-0306

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation) CRSII5_205933
6 · The paper itself

Abstract

The COVID-19 pandemic has accelerated the development and adoption of wastewater-based epidemiology. Wastewater samples can provide genomic information for detecting and assessing the spread of SARS-CoV-2 variants in communities and for estimating important epidemiological parameters such as the selection advantage of a viral variant. However, despite demonstrated successes, epidemiological data derived from wastewater suffers from potential biases. Of particular concern are shedding profiles, which can affect the relationship between true viral incidence and viral loads in wastewater. Changes in shedding between variants may decouple the established relationship between wastewater loads and clinical test data. Using mathematical modeling, simulations, and Swiss surveillance data, we demonstrate that estimates of the selection advantage of a variant are not biased by shedding profiles. We show that they are robust to differences in shedding between variants under a wide range of assumptions, and identify specific conditions under which this robustness may break down. Additionally, we demonstrate that differences in shedding only briefly affect estimates of the effective reproduction number. Thus, estimates of selective advantage and reproduction numbers derived from wastewater maintain their advantages over traditional clinical data, even when there are differences in shedding among variants.

Indexed as

COVID-19SARS-CoV-2Virus SheddingWastewaterBasic Reproduction NumberComputer SimulationHumansModels, TheoreticalPandemicsSwitzerlandViral LoadWastewater

Identifiers

PMID40796561
PMCPMC12343795

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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.