SARS-CoV-2 Aggregated Activity Level Across Ontario Canada, Measured with the US CDC Wastewater Viral Activity Level (WVAL) Metric
Bibliographic record
Abstract
Abstract The wastewater viral activity level (WVAL) was developed by the United States Centers for Disease Control and Prevention (US CDC) as a standardized metric to aggregate SARS-CoV-2 wastewater data, enabling the assessment of infection levels and trends at state/territorial, regional and national scales. This approach also facilitates comparative analysis of SARS-CoV-2 prevalence across regions. In this study, we developed and evaluated graphical methods to integrate the WVAL metric into interpretable visualizations for public health decision-making. Preliminary analysis demonstrated that WVAL values correlated strongly with clinical case counts, supporting its role as a confirmatory epidemiological measure. The WVAL framework provided a linear quantification method, allowing for the comparison of regional variations in infection patterns. This study leveraged data from the Ontario Wastewater Surveillance Initiative (Ontario WSI), which included over 100 sampling sites across seven geographical regions. Weekly mean WVAL values were computed for each site and aggregated at regional and provincial levels. In total, 59 sites contributed to the provincial WVAL calculation. The computational aggregation method followed the US CDC’s WVAL approach and was generally comparable to the Public Health Ontario (PHO) aggregation method, with the notable improvement of incorporating a linear level scale. Overall, this study demonstrated that WVAL effectively quantified SARS-CoV-2 differences at a public health regional scale. The WVAL metric proved to be a robust epidemiological tool, complementing other surveillance measures to support public health decision-making.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".