Tracking SARS-CoV-2 transmission: evaluation of the Québec wastewater surveillance program
Bibliographic record
Abstract
Québec's COVID-19 wastewater surveillance program, initiated in March 2022, was evaluated after its first year to determine its effectiveness in tracking SARS-CoV-2 transmission. The evaluation assessed the program's validity, completeness, and timeliness. As per visual analysis, results showed temporal coherence between wastewater and clinical data, although this was period dependent. Cross-correlation function analysis failed to generate consistent results and interpretable patterns between wastewater signal and clinical indicators. The percentage of unplanned missing wastewater results was minimal (ranging from 1% to 21%, with a median of 3%). Planned missing data caused by different sampling regimen required adaptation in data processing and interpretation. The median time between infection and wastewater data availability was 13.1 days, compared to 8 days for clinical data, although high variability in delays was seen between sampling sites. The wastewater data dissemination delay was the main bottleneck to provide timely information to public health authorities. This initial evaluation highlights the program's potential to generate complementary information to clinical data and can help future studies navigate the complexities of wastewater monitoring.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".