Analysis of sampling strategies for pulse loads of SARS-CoV-2: implications for wastewater-based epidemiology
Bibliographic record
Abstract
wastewater servicing area in Toronto, Ontario, Canada to explore the role that different wastewater sampling campaigns have on estimating the prevalence of SARS-CoV-2 in a population of 60,000. A stochastic wastewater and water quality model was used to evaluate the effectiveness of 11 sampling campaigns during periods of high and low COVID-19 infection among the population, tested using virtual sampling during dry-weather flow. The virtual sampling campaigns were based on the most common automatic sampler programming capabilities and widely used wastewater-based epidemiology (WBE) sampling campaigns reported in the literature. Sampling campaigns differ in weighting method (time, volume, or flow-weighted sampling), sample count, collection period, or sample time. Results suggest that grab samples should be avoided and/or that sampling campaigns with the greatest sample counts and durations are the most robust at capturing COVID-19 infection among the population. Most surprisingly, changes to the weighting method were negligible indicating that a greater number of samples, and larger sample volumes are preferred. This work suggests that investment in flow monitoring equipment for flow- or volume-weighted sampling will not improve WBE results, and that standard time based sampling is sufficient.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".