How Toronto Pearson International Airport applied lessons from SARS to develop a pandemic response plan
Bibliographic record
Abstract
When severe acute respiratory syndrome (SARS) arrived in Canada, the nation's largest airport was caught in a major crisis while public health and emergency officials worked to limit the spread of the disease. World Health Organization travel advisories recommended limiting or postponing travel to Toronto due to concerns regarding local control over the outbreaks. Toronto Pearson International Airport worked with its extended community towards the development of local emergency and continuity strategies reflective of the quickly-evolving multi-jurisdictional requirements. These strategies were developed and implemented through two consecutive waves of outbreaks over the spring and summer of 2003. The experience had a lasting effect on the airport and its related communities in terms of the evolution of its emergency and continuity programmes. Between late 2003 and 2006, a number of reviews, public commissions and enquiries delivered their findings and recommendations. The combined effect was a permanent change in the landscape within which Canadian transportation, public health, emergency response and management operate. But are the changes enough to be ready for the next possible major emergency such as a pandemic?
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".