The Role of Whole Genome Sequencing in Defining Institutional Influenza Outbreaks
Bibliographic record
Abstract
Influenza outbreaks in long-term care facilities are common and a major source of morbidity and mortality in older adults. The adequacy of the current clinical definition for identifying institutional influenza outbreaks is unclear. We performed a retrospective whole genome sequencing and epidemiologic analysis of institutional influenza outbreaks occurring during 2014–2015 influenza season in Toronto, Canada. Only outbreaks with at least two PCR-positive samples for influenza H3N2 were considered. We sequenced the two earliest submitted pairs of influenza positive samples from 39 reported institutional outbreaks in long-term care facilities. 108 of 147 H3N2 influenza outbreaks occurring during the 2014–2015 season had samples submitted to the provincial public health laboratory. Eighty-seven outbreaks had two or more PCR positive samples, and of these 39 outbreaks with satisfactory samples for genome sequencing were evaluated. Whole genome sequencing revealed that the majority of sample pairs were highly related. Inclusion of community samples demonstrated that outbreak sources were likely community introductions. Pairwise distance analysis using whole genome and HA specific genes allowed identification of thresholds for discrimination of within and between outbreak pairs, with the AUC’s ranging from 92 to 93%. The majority of outbreak pairs falling outside determined thresholds could be identified when within outbreak pairwise distance was greater than pairwise distance to a contemporaneous but separate outbreak sample. Routine whole genome sequencing for defining linked influenza outbreaks in long-term care facilities is unlikely to add significant benefit to the current clinical definition. Sequencing may prove useful for investigating specific sources of outbreak introductions. All authors: No reported disclosures.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".