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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".