Performance and suitability of wastewater based-surveillance for SARS-CoV-2 RNA in public schools
Bibliographic record
Abstract
Wastewater-based surveillance (WBS) for SARS-CoV-2 was a key strategy for epidemiological modelling and informing COVID-19 health policy during the pandemic. We assessed the capacity and performance of SARS-CoV-2 WBS in public schools. Of seventeen schools screened for participation, only four had plumbing systems that were amenable to comprehensive monitoring. From December 2020 to March 2021 composite wastewater collected twice-weekly from these four schools was compared with three municipal wastewater treatment plants (WWTPs) for SARS-CoV-2 RNA by RTqPCR and fecal biomarkers. Schools had lower rates of successful sample collection relative to WWTPs (64/79 vs. 66/66, p < 0.001). In a time of low COVID-19 activity, 13/64 of school samples were positive for SARS-CoV-2, versus 66/66 for WWTP (p < 0.0001). SARS-CoV-2 RNA in school wastewater was associated with, and often preceded, clinically confirmed COVID-19 cases among students, but showed no correlation with overall rates of student absenteeism. Levels of both SARS-CoV-2 RNA and fecal biomarkers were markedly lower in school wastewater relative to WWTPs. This work demonstrated that WBS for SARS-CoV-2 in schools can be a leading indicator of clinical disease but is technically challenging. The lower fecal biomarker levels from schools suggests children may avoid defecation at school which may further adversely impact school-based WBS for fecal-shed targets.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".