Evidence map›Paper›PMID 40396702›Full record

ArticleWater environment research : a research publication of the Water Environment Federation2025

The potential of long-term wastewater-based surveillance to predict COVID-19 waves peak in Mexico.

Marcela Zavala-Méndez, Andrés Sánchez-Pájaro, Astrid Schilmann, Juliana Calábria de Araújo, Germán Buitrón, Julián Carrillo-Reyes

Abstract read
In one paragraph

Article in Water environment research : a research publication of the Water Environment Federation, 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

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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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
    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

6 authors.

Marcela Zavala-MéndezLaboratorio de Investigación en Procesos Avanzados de Tratamiento de Aguas, Unidad Académica Juriquilla, Instituto de Ingeniería, Universidad Nacional Autónoma de México, Querétaro, México.
Andrés Sánchez-PájaroCenter for Population Health Research, National Institute of Public Health, Cuernavaca, Mexico.
Astrid SchilmannCenter for Population Health Research, National Institute of Public Health, Cuernavaca, Mexico.ORCID https://orcid.org/0000-0002-6302-4320
Juliana Calábria de AraújoDepartment of Sanitary and Environmental Engineering (DESA), Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil.ORCID https://orcid.org/0000-0002-5497-8675
Germán BuitrónLaboratorio de Investigación en Procesos Avanzados de Tratamiento de Aguas, Unidad Académica Juriquilla, Instituto de Ingeniería, Universidad Nacional Autónoma de México, Querétaro, México.ORCID https://orcid.org/0000-0003-3975-7644
Julián Carrillo-ReyesLaboratorio de Investigación en Procesos Avanzados de Tratamiento de Aguas, Unidad Académica Juriquilla, Instituto de Ingeniería, Universidad Nacional Autónoma de México, Querétaro, México.ORCID https://orcid.org/0000-0002-0431-1426

Funding

DGAPA-UNAM IN105423
6 · The paper itself

Abstract

Wastewater-based surveillance (WBS) is valuable method for monitoring the dispersion of pathogens at a low cost. However, their impact on public health decision-making is limited because there is a lack of long-term analyses, especially in low- and middle-income countries. This study aimed to assess the effectiveness of using WBS to predict the occurrence of COVID-19 waves and estimate the prevalence of infection, emphasizing the impact of SARS-CoV-2 variants. During 17 months of influent monitoring of two wastewater treatment plants in Queretaro City, Mexico, wave prediction time was influenced by variant dispersion. Waves dominated by the Delta and Omicron variants circulation showed lead days values from 5 to 14 and 1 to 4 days, respectively. According to the Monte Carlo model, disease prevalence prediction by WBS aligned with clinically reported cases at wave onsets, but the variant's transmissibility explained the overestimation during peaks. This work provides new insights into the potential and limitations of using WBS as an epidemiological tool for detecting pathogens and predicting their occurrence. PRACTITIONER POINTS: Long-term wastewater monitoring allowed early prediction of COVID-19 case waves. The prediction capability is related to the variant presence and their infectivity. The prevalence estimated by wastewater surveillance was higher in all case waves. The prevalence estimation has limitations regarding variations in data input.

Indexed as

COVID-19Environmental MonitoringSARS-CoV-2WastewaterHumansMexicoMonte Carlo MethodWastewaterMonte Carlo simulationSARS‐CoV‐2 variantsviral transmissibilitywastewater‐based surveillance

Identifiers

PMID40396702
PMCPMC12094067

What OpenQuestion holds

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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.