Seasonal Influenza Vaccine Allocation in the Canadian Population during a Pandemic
Bibliographic record
Abstract
INTRODUCTION: Emerging data suggest that receipt of the seasonal influenza vaccine may be associated with an enhanced risk of infection with pandemic (H1N1) 2009 (pH1N1). We sought to evaluate different seasonal vaccination strategies during a pandemic in the presence of varying levels of pH1N1 infection risk following seasonal influenza vaccine receipt. METHODS: We developed a deterministic, age-structured compartmental model of influenza transmission in the presence of two circulating strains (pH1N1 and seasonal). We examined the effect of different seasonal vaccination strategies on total influenza-attributable mortality in the Canadian population for the 2009-2010 influenza season. RESULTS: Seasonal vaccination strategies that focused on individuals aged >/=65 or delayed seasonal vaccine delivery until January tended to minimize mortality. In the presence of low levels (<2%) of co-circulating seasonal influenza, mortality estimates were sensitive to the seasonal vaccine-associated relative risk (RR), with small increases in RR resulting in enhanced mortality compared to the no seasonal vaccination option. Timing of the peak of pH1N1 activity and the amount of circulating seasonal influenza modified the impact of enhanced risk on total mortality. DISCUSSION: In the presence of uncertainty surrounding enhanced risk of pH1N1 acquisition with seasonal vaccine receipt, delaying seasonal vaccine delivery or restricting vaccine to individuals aged >/=65 may reduce overall influenza-attributable mortality in the Canadian population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".