Quantification and Differentiation of SARS-CoV-2 Variants in Wastewater for Surveillance
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Wastewater surveillance plays an important role in the monitoring of infections of SARS-CoV-2 at the community level. We report here the determination of SARS-CoV-2 and differentiation of its variants of concern in 294 wastewater samples collected from two major Canadian cities from May 2021 to March 2023. The overall method of analysis involved extraction of the virus and viral components using electronegative membranes, in situ stabilization and concentration of the viral RNA onto magnetic beads, and direct analysis of the viral RNA on the magnetic beads. Multiplex reverse transcription quantitative polymerase chain reaction (RT-qPCR) assays, targeting specific and naturally selected mutations in SARS-CoV-2, enabled detection and differentiation of the Alpha, Beta, Gamma, Delta, and Omicron variants. An Omicron triplex RT-qPCR assay targeting three mutations, HV 69–70 deletion, K417N, and L452R, was able to detect and differentiate the Omicron BA.1/BA.3, BA.2/XBB, and BA.4/5. This assay had efficiencies of 90–104% for all three mutation targets and a limit of detection of 28 RNA copies per reaction. Analyses of 294 wastewater samples collected over a two-year span showed the concentrations and trends of Alpha, Beta, Gamma, Delta, and Omicron variants as they emerge in two major Canadian cities participating in the wastewater surveillance program. The trends of specific variants were consistent with clinical reports for the same period. At the beginning of each wave, the corresponding variants were detectable in wastewater. For example, RNA concentrations of the BA.2 variant were as high as 10 4 copies per 100 mL of wastewater collected in January 2022, when approximately only 50–60 clinical cases of BA.2 infection were reported in Canada. These results show that the strategy and highly sensitive assays for the variants of concern in wastewater are potentially useful for the detection of newly emerging SARS-CoV-2 variants and other viruses for future community biomonitoring.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".