Assessment of Rapid and Conventional RT-qPCR-Based Systems for Wastewater Surveillance
Bibliographic record
Abstract
Conventional wastewater surveillance (WS) relies on highly trained personnel, advanced instrumentation, and significant resources, making the development and use of simple, rapid, and sensitive alternative technologies valuable for reducing costs, time, and labor intensity. For the first time, this study investigated the use of two well-developed rapid systems, including the GeneXpert and LuminUltra, in parallel with a conventional WS reference methodology for the assessment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in three cities: Saskatoon, Prince Albert, and North Battleford, Saskatchewan, Canada. RNA extractions from wastewater samples were carried out for the conventional reference and LuminUltra methods, while GeneXpert was used for both raw and concentrated wastewater samples. Bland–Altman plots showed a combination of systematic bias and random error between these real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR)-based systems. Additionally, results indicated the reasonable performance of GeneXpert in viral detection with a sensitivity rate of >98%, as compared to the conventional reference methodology of 100% and LuminUltra with >65%. A Spearman correlation test showed meaningful relationships between the GeneXpert and conventional reference methodology viral level results across all cities, indicating GeneXpert’s reliability for accurate viral detection and disease prevalence determination, specifically in limited-resource communities, with a shorter processing time and cost-effectiveness in analysis.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".