P-1953. Youden’s Index Ensures the Accuracy of Wastewater-based Surveillance of SARS-CoV-2 Variants by Allele-Specific RT-qPCR or Genomic Sequencing
Bibliographic record
Abstract
Abstract Background Clinical genomic surveillance is the gold standard for monitoring SARS-CoV-2 variants globally, but as the pandemic wanes, reduced testing increases the risk of missing the emergence of variants of concern or failing to accurately follow their trajectory in populations. Wastewater-based genomic surveillance (WWS) that estimates variant frequency based on its defining set of alleles derived from clinical genomic surveillance has been successfully implemented. However, this method has its challenges, and allele-specific (AS) RT-qPCR that monitors a single, variant-defining, allele is used as a complementary method for estimating variant prevalence. Demonstrating equivalent performance of the methods is a prerequisite for their continued application of WWS in current and future pandemics.Figure 1:Comparison of AS-RT-qPCR and amplicon-based sequencing methods for estimating SARS-CoV-2 variants frequency in wastewaters; A) N: D63G and B.1.617.2 haplotype, B) N: P13L and B.1.1.529 haplotype, C) S: H69+/V70+ and BA.1 haplotype, and D) S: H69-/V70- and BA.2 haplotype. Methods We compared single-allele frequency estimation using AS-RT-qPCR, to single-allele or haplotype frequency estimations derived from amplicon-based sequencing to estimate variant prevalence in municipal wastewater collected during emergent and prevalent periods of Delta, Omicron, and two of its sub-lineages in Ottawa, Canada.Table 1:Youden index of targeted alleles (N: D63G, N: P13L, S: H69+/V70+ and S: H69-/V70-) in wastewaters using AS-RT-qPCR and sequencing as well as haplotype of each variant. Results We found that all three methods of frequency estimation were concordant and contained sufficient information to describe the trajectory of variant prevalence in wastewater across time (Figure 1). We further evaluated the accuracy of these methods by quantifying the diagnostic performance (i.e., method accuracy expressed as Youden’s index), based on available clinical genomic prevalence data. Youden’s index confirms the accuracy of methods employed for variant frequency estimation in wastewater (Table 1), but accuracy of each method can be influenced by different factors. Conclusion WWS emerges as a crucial epidemiological tool for monitoring infectious disease in the population during the COVID-19 pandemic. This study validates the accuracy of WWS in SARS-CoV-2 variants monitoring using AS-RT-qPCR or sequencing methods and provides comprehensive perspective for implementing public health interventions against infectious diseases. Disclosures All Authors: No reported disclosures
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".