Quantitative Trend Analysis of SARS-CoV-2 RNA in Municipal Wastewater Exemplified with Sewershed-Specific COVID-19 Clinical Case Counts
Why this work is in the frame
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Bibliographic record
Abstract
We demonstrate a new methodology for quantitative trend analysis (QTA) to analyze and interpret SARS-CoV-2 RNA wastewater surveillance results concurrently with clinical case data. This demonstration is based on the work completed under the Ontario (Canada) Wastewater Surveillance Initiative (WSI) by two laboratories in four wastewater treatment plants (WWTPs) at each of four large sewersheds, which were sampled over a 9-month period, along with sewershed-specific clinical case counts. The data from the last 5-months, representing a range of high and low case counts, was used for this demonstration. The QTA integrated clinical and wastewater virus signals, while combining recommendations from the United States Centers for Disease Control and Prevention (US CDC) and the Public Health Agency of Canada (PHAC). The key steps in the QTA consisted of signal normalization with pepper mild mottle virus (PMMoV), as a fecal biomarker, statistical linear break-point trend analysis and integration of both wastewater virus signal and clinical cases trend results. Using this approach, the wastewater virus and clinical cases trends, direction, and magnitude were clearly identified and provided a unified complementary tool to support public health decisions on a targeted, sewershed-specific basis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it