Evidence map›Paper›PMID 40893198›Full record

ArticleFrontiers in public health2025

Temporal dynamics of SARS-CoV-2 detection in wastewater and population infection trends in Mexico City.

Miguel Atl Silva-Magaña, Marisa Mazari-Hiriart, Adalberto Noyola, Ana C Espinosa-García, Guillermo de Anda-Jáuregui, Enrique Hernández-Lemus

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Miguel Atl Silva-MagañaComputational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico.
Marisa Mazari-HiriartLaboratorio Nacional de Ciencias de la Sostenibilidad, Instituto de Ecología, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Adalberto NoyolaInstituto de Ingeniería, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Ana C Espinosa-GarcíaLaboratorio Nacional de Ciencias de la Sostenibilidad, Instituto de Ecología, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Guillermo de Anda-JáureguiComputational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico.
Enrique Hernández-LemusComputational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wastewater-based epidemiology (WBE) provides a non-invasive, community-level approach to monitor infectious diseases such as COVID-19. This study investigated the temporal relationship between SARS-CoV-2 RNA levels in wastewater and reported COVID-19 cases in adjacent populations in Mexico City. A total of 40 samples were collected from the Copilco neighborhood during two epidemiological waves (April-September 2021 and November 2021-February 2022). An optimized one-step RT-qPCR protocol targeting the N1 gene achieved 96.7% efficiency with a detection limit of 10 copies/μL. Spatial classification identified three proximity zones based on drainage system topology. Cross-correlation analysis between viral genome copies and confirmed case data revealed a significant temporal lag of 6-8 days. These results support the application of WBE as an early-warning tool to inform public health strategies and anticipate infection trends.

Indexed as

COVID-19SARS-CoV-2WastewaterWastewater-Based Epidemiological MonitoringHumansMexicoRNA, ViralRNA, ViralWastewaterCOVID-19early signalspredictive modelsSARS-CoV2temporal dynamicswaste-water based epidemiology

Identifiers

PMID40893198
PMCPMC12391079

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.