ReviewJournal of environmental chemical engineering2021
Concentration techniques tailored for the detection of SARS-CoV-2 genetic material in domestic wastewater and treatment plant sludge: A review.
Review in Journal of environmental chemical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Recent progress on wastewater-based epidemiology for COVID-19 surveillance: A systematic review of analytical procedures and epidemiological modeling.The Science of the total environment · 2023Pooled it
- Comparative Efficiency of High-Throughput Magnetic Bead Method and Polyethylene Glycol Precipitation for Viral Concentration in Sewage.Food and environmental virology · 2025Article
- An interpretative review of the wastewater-based surveillance of the SARS-CoV-2: where do we stand on its presence and concern?Frontiers in microbiology · 2024Review
- Evaluation of SARS-CoV-2 concentrations in wastewater and river water samples.Case studies in chemical and environmental engineering · 2022Article
- A critical review on the existing wastewater treatment methods in the COVID-19 era: What is the potential of advanced oxidation processes in combatting viral especially SARS-CoV-2?Journal of water process engineering · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Upon the outbreak of COVID-19 pandemic, detection and quantification of SARS-CoV-2 genetic material in domestic wastewater have led to an increase in the efforts to define and implement the wastewater-based epidemiology (WBE). This application provides valuable information to define local contamination monitoring, emergence of COVID-19 and its variants and many other aspects to cope with and control the pandemic. WBE surveillance, however, requires several consecutive steps such as sampling, pretreatment and concentration of samples, and detection and quantification of SARS-CoV-2 genetic material in wastewater. In this review paper, the literature regarding to all these applications reviewed considering their advantages, disadvantages as well as their applicability. A specific emphasis was placed on the last step, detection and quantification since it covers the most critical procedure for concentrating the virus before measurement. Evaluation of the existing data indicating ultrafiltration, polyethylene glycol (PEG) precipitation and electronegative membrane filtration (ENMF) were the most promising techniques for concentration. The ongoing studies are proposed to be continued within the context of standard methods. Future research needs are delineated and suggestions are made for details.
Indexed as
Identifiers
What OpenQuestion holds
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.