Canada in the face of the 2009 H1N1 pandemic
Bibliographic record
Abstract
BACKGROUND: Initial public health responses to the 2009 influenza H1N1 pandemic were based on difficult decisions in the face of substantial uncertainty. Policy effectiveness depends critically on such decisions, and future planning for maximum protection of community health requires understanding of the impact of public health responses in observed scenarios. OBJECTIVES: In alignment with the objectives of the Pandemic Influenza Outbreak Research Modelling Team (Pan-InfORM) and the Centre for Disease Modelling (CDM), a focused workshop was organized to: (i) evaluate Canada's response to the spring and autumn waves of the novel H1N1 pandemic; (ii) learn lessons from public health responses, and identify challenges that await public health planners and decision-makers; and (iii) understand how best to integrate resources to overcome these challenges. MAIN OUTCOME MEASURES: We report on key presentations and discussions that took place to achieve the objectives of the workshop. CONCLUSIONS: Future emerging infectious diseases are likely to bring far greater challenges than those imposed by the 2009 H1N1 pandemic. Canada must address these challenges and enhance its capacity for emergency responses by integrating modelling, surveillance, planning, and decision-making.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".