Metagenomic Meta-Analysis of Antibiotic-Resistance Genes in Wastewater: A Perspective from the COVID-19 Pandemic
Bibliographic record
Abstract
Antibiotic resistance is a silent global crisis intensified by the recent pandemic of coronavirus disease 2019 (COVID-19). To address this growing threat, wastewater-based surveillance (WBS) is emerging as a promising public health tool for monitoring antibiotic resistance within communities. Our meta-analysis aims to reveal the landscape of antibiotic-resistance genes (ARGs) in global wastewater during and after the COVID-19 pandemic. The analysis included wastewater samples collected between 2020 and 2024 from five countries across three continents: Asia (China), Europe (United Kingdom and Russia), and North America (United States and Canada). Our findings showed higher observed ARGs in Russia and China despite their small sample size, while the USA showed more diverse ARGs. Distinct patterns of ARGs were observed in European and North American wastewater samples (p-value < 0.001). We identified 2483 ARGs, with multidrug-resistant (MDR) genes dominating most regions and accounting for almost 45% of all ARGs detected in Europe. Country-specific indicator ARGs showed 22 unique ARGs for Russia, 3 for each of the UK and Canada, and 2 were specific for China. Continentally, 100 indicator ARGs were specific to Asia, 38 to Europe, and 18 to North America. These findings highlight the regional variations in ARG profiles, emphasizing the urgent need for region-specific strategies to combat antibiotic-resistance threat. Additionally, our study further supports the value of WBS as a valuable public health tool for monitoring antibiotic resistance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".