Relative pandemic severity in Canada and four peer nations during the SARS-CoV-2 pandemic
Bibliographic record
Abstract
Background: National responses to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic have been highly variable. We sought to explore the effectiveness of the Canadian pandemic response up to May 2022 relative to responses in four peer countries with similar political, economic and health systems, and with close historical and cultural ties to Canada. Methods: We used reported age-specific mortality data to generate estimates of pandemic mortality standardized to the Canadian population. Age-specific case fatality, hospitalization, and intensive care admission probabilities for the Canadian province of Ontario were applied to estimated deaths, to calculate hospitalizations and intensive care admissions averted by the Canadian response. Health impacts were valued in both monetary terms, and in terms of lost quality-adjusted life years. Results: We estimated that the Canadian pandemic response averted 94,492, 64,306 and 13,641 deaths relative to the responses of the United States, United Kingdom and France, respectively, and more than 480,000 hospitalizations relative to the United States. The United States pandemic response, if applied to Canada, would have resulted in more than $40 billion in economic losses due to healthcare expenditures and lost quality-adjusted life years. In contrast, an Australian pandemic response applied to Canada would have averted over 28,000 additional deaths and averted nearly $9 billion in costs. Conclusion: Canada outperformed several peer countries that aimed for mitigation rather than elimination of SARS-CoV-2 in the first two years of the pandemic, with substantial numbers of lives saved and economic costs averted. However, a comparison with Australia demonstrated that an elimination focus would have saved Canada tens of thousands of lives as well as substantial economic costs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".