Unveiling the global urban virome through wastewater metagenomics
Bibliographic record
Abstract
Understanding global viral dynamics is critical for public health. Traditional surveillance focuses on individual pathogens and symptomatic cases, which may miss asymptomatic infections or newly emerging viruses, delaying detection and response. Wastewater-based epidemiology has been used to track pathogens through targeted molecular assays, but its reliance on predefined targets limits detection of the full viral spectrum. Here, we analyse longitudinal wastewater samples from 62 cities across six continents (2017-2019) using metagenomics and capture-based sequencing with probes targeting viruses associated with gastrointestinal disease. We detect over 2500 viral species spanning 122 families, many with human, animal, or plant health relevance. The bacteriophage family Microviridae and plant virus family Virgaviridae dominate the metagenomic dataset, while Astroviridae and Picornaviridae prevail in the capture-based sequence dataset. Virus distributions are broadly similar across continents at the family and genus levels, yet distinct city-level fingerprints reveal geographical and temporal variation, enabling spatiotemporal surveillance of viruses such as astroviruses and enteroviruses. Global wastewater-based epidemiology enables early detection of emerging viruses, including Echovirus 30 in Europe and Tomato brown rugose fruit virus. These findings highlight the potential of wastewater sequencing for the early detection of emerging viruses and population-wide virome monitoring across diverse hosts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".