Wastewater-Based Epidemiology of SARS-CoV-2 and Other Respiratory Viruses: Bibliometric Tracking of the Last Decade and Emerging Research Directions
Bibliographic record
Abstract
The COVID-19 pandemic has prompted an overwhelming surge in research investigating different aspects of the disease and its causative agent. In this study, we aim to discern research themes and trends in the field of wastewater-based epidemiology (WBE) of SARS-CoV-2 and other respiratory viruses over the past decade. We examined 904 papers in the field authored by researchers from 87 countries. Despite the low reported incidence of COVID-19 in 2023, researchers are still interested in the application of WBE to SARS-CoV-2. Based on network visualization mapping of 189 keyword co-occurrences, method optimization, source, transmission, survival, surveillance or early-warning detection systems, and variants of concern in wastewater were found to be the topics of greatest interest among WBE researchers. A trend toward evaluations of the utility of new technologies such as digital PCR and WBE for other respiratory viruses, particularly influenza, was observed. The USA emerged as the leading country in terms of research publications, citations, and international collaborations. Additionally, Science of the Total Environment stood out as the journal with the highest number of publications and citations. The study highlighted areas for further research, including data normalization and biosensor-based data collection, and emphasized the need for international collaboration and standardized methodology for WBE in future research directions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".