National assessment of Canadian pandemic preparedness: Employing InFluNet to identify high-risk areas for inter-wave vaccine distribution
Bibliographic record
Abstract
Influenza pandemics emerge at irregular and unpredictable intervals to cause substantial health, economic and social burdens. Optimizing health-system response is vital to mitigating the consequences of future pandemics. We developed a mathematical model to assess the preparedness of Canadian health systems to accommodate pandemic-related increases in patient demand. We identify vulnerable areas, assess the potential of inter-wave vaccination to mitigate impacts and evaluate the association between demographic and health-system characteristics in order to identify predictors of pandemic consequences. Modelled average attack rates were 23.7–37.2% with no intervention and 2.5–6.4% with pre-vaccination. Peak acute-care demand was 7.5–19.5% of capacity with no intervention and 0.6–2.6% with pre-vaccination. The peak ICU demand was 39.3–101.8% with no intervention and 2.9–13.3% with pre-vaccination. Total mortality was 2258–7944 with no intervention and 88–472 with pre-vaccination. Regions of Southern Ontario were identified as most vulnerable to surges in patient demand. The strongest predictors of peak acute-care demand and ICU demand were acute-care bed capacity (R = −0.8697; r2 = 0.7564) and ICU bed capacity (R = −0.8151; r2 = 0.6644), respectively. Demographic characteristics had mild associations with predicted pandemic consequences. Inter-wave vaccination provided adequate acute-care resource protection under all scenarios; ICU resource adequacy was protected under mild disease assumptions, but moderate and severe diseases caused demand to exceed expected availability in 21% and 49% of study areas, respectively. Our study informs priority vaccine distribution strategies for pandemic planning, emphasizing the need for targeted early vaccine distribution to high-risk individuals and areas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".