The Urban Water Cycle as a Planning Tool to Monitor SARS-CoV-2: A Review of the Literature
Bibliographic record
Abstract
COVID-19 is a terrible virus that has impacted human health and the economy on a global scale. The detection and control of the pandemic have become necessities that require appropriate monitoring strategies. One of these strategies involves measuring and quantifying the virus in water at different stages of the Urban Water Cycle (UWC). This article presents a comprehensive literature review of the analyses and quantifications of SARS-CoV-2 in multiple UWC components from 2020 to June 2021. More than 140 studies worldwide with a focus on industrialized nations were identified, mainly in the USA, Australia, and Asia and the European Union. Wastewater treatment plants were the focus of most of these studies, followed by city sewerage systems and hospital effluents. The fewest studies examined the presence of this virus in bodies of water. Most of the studies were conducted for epidemiological purposes. However, a few focused on viral load and its removal using various treatment strategies or modelling and developing strategies to control the disease. Others compared methodologies for determining if SARS-CoV-2 was present or included risk assessments. This is the first study to emphasize the importance of the various individual components of the UWC and their potential impacts on viral transmission from the source to the public.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".