ArticleCase studies in chemical and environmental engineering2022
Advances in virus detection methods for wastewater-based epidemiological applications.
Article in Case studies in chemical and environmental engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Wastewater Surveillance for Early Warning of Infectious Disease Outbreaks: A Systematic Review of Evidence and Implications for One Health Surveillance.Pathogens (Basel, Switzerland) · 2026Pooled it
- Review
- Algal-based membrane bioreactors: a sustainable Frontier for removing emerging pollutants from wastewater.RSC advances · 2025Review
- Integrated biodiesel and biopolymer production fromRSC advances · 2025Article
- A Narrative Review of High Throughput Wastewater Sample Processing for Infectious Disease Surveillance: Challenges, Progress, and Future Opportunities.International journal of environmental research and public health · 2024Review
- SARS-CoV-2 detection and inactivation in water and wastewater: review on analytical methods, limitations and future research recommendations.Emerging microbes & infections · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wastewater-based epidemiology (WBE) is a powerful tool that has the potential to reveal the extent of an ongoing disease outbreak or to predict an emerging one. Recent studies have shown that SARS-CoV-2 concentration in wastewater may be correlated with the number of COVID-19 cases in the corresponding population. Most of the recent studies and applications of wastewater-based surveillance of SARS-CoV-2 applied the "gold standard" real-time quantitative reverse transcription-polymerase chain reaction (RT-qPCR) detection method. However, this method also has its limitations. The paper aimed to present recent improvements and applications of the PCR-based methods for SARS-CoV-2 monitoring in wastewater. Furthermore, it aimed to review alternative methods utilized and/or proposed for the detection of the virus in wastewater matrices. From the review, it was found that several studies have investigated the use of reverse-transcription digital polymerase reaction (RT-dPCR), which was generally shown to have a lower limit of detection (LOD) over the RT-qPCR. Aside from this, non-PCR-based and non-RNA based methods have also been explored for the detection of SARS-CoV-2 in wastewater, with detailed attention given to the detection of SARS-CoV-2 proteins. The potential methods for protein detection include mass spectrometry, the use of immunosensors, and nanotechnological applications. In addition, the review of recent studies also revealed two types of emerging methods related to the detection of SARS-CoV-2 in wastewater:
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Registered trials
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.