Wastewater surveillance of influenza activity: Early detection, surveillance, and subtyping in city and neighbourhood communities
Bibliographic record
Abstract
Abstract Recurrent epidemics of influenza infection and its pandemic potential present a significant risk to global population health. To mitigate hospitalizations and death, local public health relies on clinical surveillance to locate and monitor influenza-like illnesses and/or influenza cases and outbreaks. At an international level, the global integration of clinical surveillance networks is the only reliable method to report influenza types and subtypes and warn of an emergent pandemic strain. During the COVID-19 pandemic, the demonstrated utility of wastewater surveillance (WWS) in complementing or even replacing clinical surveillance, the latter a resource-intensive enterprise, was predicated on the presence of stable viral fragments in wastewater. We show that influenza virus targets are stable in wastewaters and partitions to the solids fraction. We subsequently quantify, type, and subtype influenza virus in municipal wastewater and primary sludge throughout the course of a community outbreak. This research demonstrates the feasibility of applying influenza virus WWS to city and neighbourhood levels; showing a 17-day lead time in forecasting a citywide flu outbreak and providing population-level viral subtyping in near real-time using minimal resources and infrastructure.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".