Evidence map›Paper›PMID 36162580›Full record

ArticleThe Science of the total environment2023

Citywide wastewater SARS-CoV-2 levels strongly correlated with multiple disease surveillance indicators and outcomes over three COVID-19 waves.

Loren Hopkins, David Persse, Kelsey Caton, Katherine Ensor, Rebecca Schneider, Camille McCall, Lauren B Stadler

Open access · greenAbstract read
In one paragraph

Article in The Science of the total environment, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
5.9field-weighted citation impact, top 3% of its field
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

30 citing papers in PubMed, 61 citations in OpenAlex.

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  9. Dynamics of SARS-CoV-2 variants in southwest Ohio municipal wastewater.Environmental science : water research & technology · 2025
    Article
  10. The potential of long-term wastewater-based surveillance to predict COVID-19 waves peak in Mexico.Water environment research : a research publication of the Water Environment Federation · 2025
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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

7 authors at 3 institutions in 1 country.

Loren HopkinsHouston Health Department, 8000 N. Stadium Dr., Houston, TX, United States of America; Department of Statistics, Rice University, 6100 Main Street MS 138, Houston, TX, United States of America.
David PersseHouston Health Department, 8000 N. Stadium Dr., Houston, TX, United States of America; Department of Medicine and Surgery, Baylor College of Medicine, Houston, TX, United States of America; City of Houston Emergency Medical Services, Houston, TX, United States of America.
Kelsey CatonHouston Health Department, 8000 N. Stadium Dr., Houston, TX, United States of America.
Katherine EnsorDepartment of Statistics, Rice University, 6100 Main Street MS 138, Houston, TX, United States of America.
Rebecca SchneiderHouston Health Department, 8000 N. Stadium Dr., Houston, TX, United States of America.
Camille McCallDepartment of Civil and Environmental Engineering, Rice University, 6100 Main Street MS-519, Houston, TX, United States of America.
Lauren B StadlerDepartment of Civil and Environmental Engineering, Rice University, 6100 Main Street MS-519, Houston, TX, United States of America. Electronic address: lauren.stadler@rice.edu.
Houston Health and Human Services Department · USRice University · USBaylor College of Medicine · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Public health surveillance systems for COVID-19 are multifaceted and include multiple indicators reflective of different aspects of the burden and spread of the disease in a community. With the emergence of wastewater disease surveillance as a powerful tool to track infection dynamics of SARS-CoV-2, there is a need to integrate and validate wastewater information with existing disease surveillance systems and demonstrate how it can be used as a routine surveillance tool. A first step toward integration is showing how it relates to other disease surveillance indicators and outcomes, such as case positivity rates, syndromic surveillance data, and hospital bed use rates. Here, we present an 86-week long surveillance study that covers three major COVID-19 surges. City-wide SARS-CoV-2 RNA viral loads in wastewater were measured across 39 wastewater treatment plants and compared to other disease metrics for the city of Houston, TX. We show that wastewater levels are strongly correlated with positivity rate, syndromic surveillance rates of COVID-19 visits, and COVID-19-related general bed use rates at hospitals. We show that the relative timing of wastewater relative to each indicator shifted across the pandemic, likely due to a multitude of factors including testing availability, health-seeking behavior, and changes in viral variants. Next, we show that individual WWTPs led city-wide changes in SARS-CoV-2 viral loads, indicating a distributed monitoring system could be used to enhance the early-warning capability of a wastewater monitoring system. Finally, we describe how the results were used in real-time to inform public health response and resource allocation.

Indexed as

COVID-19SARS-CoV-2HumansPandemicsRNA, ViralWastewaterRNA, ViralWastewaterCOVID-19HospitalizationsLead timePositivity rateSARS-CoV-2Syndromic surveillanceWastewater-based epidemiology

Identifiers

PMID36162580
PMCPMC9507781
OpenAlexW4297035788

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.