Canary in the Coal Mine: Two-Year Wastewater Surveillance Results from an Indicator Community for Early Indication of IAV, RSV, Mpox, and SARS-CoV-2 in Five Surrounding Towns and Cities
Bibliographic record
Abstract
An indicator community (IC) is a community in a larger population where pathogens can be observed earlier due to its unique characteristics, similar to the canary in the coal mine analogy. This study investigated using a strategically selected IC as an early viral indicator for the City itself and five surrounding towns and cities. Wastewater surveillance was carried out at least three times per week for 2 years to track IAV, RSV, mpox, and SARS-CoV-2. The IC, a university campus, was selected based on its closed-loop sewer system, population demographics, and high social connectivity. Results showed that wastewater surveillance at a well-selected IC can serve as an early indicator of emerging and seasonal viruses, providing a necessary lead time of at least 1 week for timely public health interventions. While wastewater treatment plants typically offer 1–3 days of early detection, the IC approach extended this window to 1–3 weeks. The IC approach was particularly useful as an early indicator of new surges and outbreaks of seasonal respiratory viruses. The results also showed a consistent presence of SARS-CoV-2 wastewater viral signal in all communities, unlike the seasonal respiratory viruses, indicating an ongoing low-level transmission and circulation of SARS-CoV-2 throughout the year.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".