Bibliographic record
Abstract
ABSTRACT This paper estimates the outcomes of two different COVID-19 vaccination strategies in Canada for the mRNA vaccines currently approved for Emergency Use Authorization (EUA), modelled on the vaccination and effectiveness of the Pfizer vaccine which is likely to be more widely administered in Canada. The first strategy is the manufacturer recommended standard of two doses (two-dose strategy) given within 21 days apart versus a strategy of giving a larger group a single dose of vaccine (first-dose-for-most strategy) by delaying the second injection. Three parameters are varied in the course of 36 estimation scenarios of the population-level effects of the two vaccination strategies. The first is the effectiveness of a single dose of vaccine at preventing disease, the second is the effectiveness of the vaccine at preventing transmission of the virus, and the third is the rate of transmission of the virus during the course of the simulations. Over the course of the different scenarios, the first-dose-for-most strategy was superior in reducing disease transmission in all scenarios where vaccination is assumed to have an effect on viral transmission. The results for fatalities was mixed, with the first-dose-for-most strategy being superior in cases where a higher first-dose effectiveness at preventing disease was assumed. Finally, in the best-guess scenarios where a 75% reduction in disease transmission and a 92.6% effectiveness at preventing disease from a single dose were used, the first-dose-for-most strategy was superior in a situation with reduced vaccine doses available, and switching to the first-dose-for-most strategy earlier helped to prevent a higher proportion of cases and deaths.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".