Diagnostic performance of allele-specific RT-qPCR and genomic sequencing in wastewater-based surveillance of SARS-CoV-2
Bibliographic record
Abstract
Clinical genomic surveillance is regarded as the gold standard for monitoring SARS-CoV-2 variants globally. However, as the pandemic wanes, reduced testing poses a risk to effectively tracking the trajectory of these variants within populations. Wastewater-based genomic surveillance that estimates variant frequency based on its defining set of alleles derived from clinical genomic surveillance has been successfully implemented. This method has its challenges, and allele-specific (AS) RT-qPCR or RT-dPCR may instead be used as a complementary method for estimating variant prevalence. Demonstrating equivalent performance of these methods is a prerequisite for their continued application in current and future pandemics. Here, we compared single-allele frequency using AS-RT-qPCR, to single-allele or haplotype frequency estimations derived from amplicon-based sequencing to estimate variant prevalence in wastewater during emergent and prevalent periods of Delta, Omicron, and two sub-lineages of Omicron. We found that all three methods of frequency estimation were concordant and contained sufficient information to describe the trajectory of variant prevalence. We further confirmed the accuracy of these methods by quantifying the diagnostic performance through Youden's index. The Youden's index of AS-RT-qPCR was reduced during the low prevalence period of a particular variant while the same allele in sequencing was negatively influenced due to insufficient read depth. Youden's index of haplotype-based calls was negatively influenced when alleles were common between variants. Coupling AS-RT-qPCR with sequencing can overcome the shortcomings of either platform and provide a comprehensive picture to the stakeholders for public health responses.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".