Multiplex Assays Enable Simultaneous Detection and Identification of SARS-CoV-2 Variants of Concern in Clinical and Wastewater Samples
Bibliographic record
Abstract
The targeted screening and sequencing approaches for COVID-19 surveillance need to be adjusted to fit the evolving surveillance objectives which necessarily change over time. We present the development of variant screening assays that can be applied to new targets in a timely manner and enable multiplexing of targets for efficient implementation in the laboratory. By targeting the HV69/70 deletion for Alpha, K417N for Beta, K417T for Gamma, and HV69/70 deletion plus K417N for sub-variants BA.1, BA.3, BA.4, and BA.5 of Omicron, we achieved simultaneous detection and differentiation of Alpha, Beta, Gamma, and Omicron in a single assay. Targeting both T478K and P681R mutations enabled specific detection of the Delta variant. The multiplex assays used in combination, targeting K417N and T478K, specifically detected the Omicron sub-variant BA.2. The limits of detection for the five variants of concern were 4-16 copies of the viral RNA per reaction. Both assays achieved 100% clinical sensitivity and 100% specificity. Analyses of 377 clinical samples and 24 wastewater samples revealed the Delta variant in 100 clinical samples (nasopharyngeal and throat swab) collected in November 2021. Omicron BA.1 was detected in 79 nasopharyngeal swab samples collected in January 2022. Alpha, Beta, and Gamma variants were detected in 24 wastewater samples collected in May-June 2021 from two major cities of Alberta (Canada), and the results were consistent with the clinical cases of multiple variants reported in the community.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".