The molecular epidemiology of respiratory syncytial virus in Ontario, Canada from 2022–2024 using a custom whole genome sequencing assay and analytics package
Bibliographic record
Abstract
BACKGROUND: Respiratory Syncytial Virus (RSV) infections are a cause of significant morbidity and mortality in children and the elderly. Despite the clinical burden of disease, very little is known about the inter- and intra-seasonal genomic variability of RSV. Furthermore, the recent approval of vaccines and monoclonal antibody therapies will likely lead to higher selective pressure on RSV. Genomic surveillance will be essential to monitor viral changes and inform future therapeutic developments and public health responses. Here, we describe the development of an amplicon-based whole-genome sequencing assay for RSV to enable genomic surveillance. METHODS: A 750-bp overlapping amplicon design was developed to co-amplify RSV-A/-B directly from patient samples collected during two respiratory illness seasons (2022/23, 2023/24) for whole-genome sequencing. RSV subtype, clade, and F-protein antigenic site sequences were determined with a custom analytical pipeline. RESULTS: Of the 429 specimens included in the study 410 (95.6 %) samples met acceptability. Our data demonstrated co-circulation of both RSV subtypes, with increasing predominance of RSV-A since 2022. There were seven genomic clades of RSV-A, while >95 % of RSV-B belonged to a single clade. 1.5 % of samples had amino acid changes within the binding sites of the current RSV therapeutics Palivizumab or Nirsevimab. CONCLUSIONS: Continuous monitoring of RSV genotypes and mutations will be critical for understanding the impact of new therapeutics and vaccines on RSV epidemiology and detecting emergence of vaccine-escape and/or antiviral resistant mutations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".