Wastewater Surveillance for Seasonal Influenza Epidemics: Strategies and Considerations for Small Public Health Units
Bibliographic record
Abstract
To reduce the amount of testing and cost necessary to generate representative wastewater surveillance of influenza A virus (IAV) data for small public health units (PHU) in large geographic areas with small and dispersed municipalities, we compared the wastewater (WW) viral activity level (VAL) metric, developed by the United States Centers of Disease Control and Prevention (CDC) to the raw data and viral load to better interpret the relationship between WW signal and weekly number of positive clinical cases. We assessed two small PHUs in Ontario, Canada, and with just 21–27% coverage of the PHUs’ populations, WW surveillance for IAV viral RNA, viral load, and raw WW signals was able to obtain strong positive Kendall’s τ correlations with PHUs’ IAV clinical cases, showing (0.59–0.85) and (0.77–0.93), respectively. The VAL also helped identify towns with higher-than-expected levels of IAV. Measurement of other WW parameters and assessment of sewer infrastructure provided explanations for the differences observed between each WW treatment plant and its respective PHU. Overall, we demonstrated that minimal sampling within a small PHU, supported by careful consideration of sewer infrastructure and the location of WW treatment plants, can provide an accurate, efficient, and cost-effective approach for IAV surveillance.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".