A Genome Sequence Variant Monitoring Program for Seasonal Influenza A H3N2 and Respiratory Syncytial Virus A using Wastewater-Based Surveillance in Ontario, Canada
Bibliographic record
Abstract
Abstract Seasonal respiratory viruses, such as the Influenza A virus and the respiratory syncytial virus, are responsible for over a billion infections worldwide each year resulting in a substantial burden on health care systems. Surveillance of these viruses, including their prevalence in communities and their evolution, are essential for informing public health decisions and recommending vaccine formulations and schedules. Typically, these viruses are monitored using clinical samples from patients seeking medical attention. Recently, wastewater-based surveillance (WBS) has been leveraged to understand transmission dynamics and genome evolution of SARS-CoV-2 and seasonal respiratory viruses. To further the utility of WBS we developed and implemented novel tiled-amplicon sequencing assays to identify and track Influenza A virus H3N2 and respiratory syncytial virus A circulating in Southern Ontario, Canada. We also developed virus specific deconvolution tools to estimate the abundance of mixed lineages in wastewater. These assays were able to accurately determine which lineages were circulating in wastewater with high sensitivity and specificity. If implemented in regular surveillance programs, they could be used to inform real-time public health decisions and determine potential disease surge with impact on emergency room visits and hospitalization, as well as track which emerging strains will become predominant in the future and determine which strains should be the focus of seasonal vaccines.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".