Evidence map›Paper›PMID 40128707›Full record

ArticleBMC public health2025

The use of wastewater surveillance to estimate SARS-CoV-2 fecal viral shedding pattern and identify time periods with intensified transmission.

Wan Yang, Enoma Omoregie, Aaron Olsen, Elizabeth A Watts, Hilary Parton, Ellen Lee

Abstract read
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Wan YangDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA. wy2202@cumc.columbia.edu.
Enoma OmoregieNew York City Department of Health and Mental Hygiene, Queens, NY, USA.
Aaron OlsenNew York City Department of Health and Mental Hygiene, Queens, NY, USA.
Elizabeth A WattsNew York City Department of Health and Mental Hygiene, Queens, NY, USA.
Hilary PartonNew York City Department of Health and Mental Hygiene, Queens, NY, USA.
Ellen LeeNew York City Department of Health and Mental Hygiene, Queens, NY, USA.

Funding

Disease Persistence and Population Dynamics: Modeling Measles under Mass VaccinationR01AI145883 · NIAID · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI YANG, WAN · 2019 to 2024
$2.5M
Using wastewater surveillance data to study SARS-CoV-2 dynamics and predict COVID-19 outcomesR21AI175747 · NIAID · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI YANG, WAN · 2023 to 2024
$452k
Council of State and Territorial Epidemiologists NU38OT00297National Institute of Allergy and Infectious Diseases AI175747NIAID NIH HHS R01 AI145883NIAID NIH HHS R21 AI175747
6 · The paper itself

Abstract

backgroundWastewater-based surveillance is an important tool for monitoring the COVID-19 pandemic. However, it remains challenging to translate wastewater SARS-CoV-2 viral load to infection number, due to unclear shedding patterns in wastewater and potential differences between variants.

objectivesWe utilized comprehensive wastewater surveillance data and estimates of infection prevalence (i.e., the source of the viral shedding) available for New York City (NYC) to characterize SARS-CoV-2 fecal shedding pattern over multiple COVID-19 waves.

methodsWe collected SARS-CoV-2 viral wastewater measurements in NYC during August 31, 2020 - August 29, 2023 (N = 3794 samples). Combining with estimates of infection prevalence (number of infectious individuals including those not detected as cases), we estimated the time-lag, duration, and per-infection fecal shedding rate for the ancestral/Iota, Delta, and Omicron variants, separately. We also developed a procedure to identify occasions with intensified transmission.

resultsModels suggested fecal viral shedding likely starts around the same time as and lasts slightly longer than respiratory tract shedding. Estimated fecal viral shedding rate was highest during the ancestral/Iota variant wave, at 1.44 (95% CI: 1.35 - 1.53) billion RNA copies in wastewater per day per infection (measured by RT-qPCR), and decreased by around 20% and 50-60% during the Delta wave and Omicron period, respectively. We identified around 200 occasions during which the wastewater SARS-CoV-2 viral load exceeded the expected level in any of the city's 14 sewersheds. These anomalies disproportionally occurred during late January, late April-early May, early August, and from late-November to late-December, with frequencies exceeding the expectation assuming random occurrence (P < 0.05; bootstrapping test). DISCUSSION: These estimates may be useful in understanding changes in underlying infection rate and help quantify changes in COVID-19 transmission and severity over time. We have also demonstrated that wastewater surveillance data can support the identification of time periods with potentially intensified transmission.

Indexed as

COVID-19FecesSARS-CoV-2Virus SheddingWastewaterWastewater-Based Epidemiological MonitoringHumansNew York CityTime FactorsWastewaterCOVID-19TransmissionVariantViral shedding patternWastewater surveillance

Identifiers

PMID40128707
PMCPMC11931833

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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.