Modelled Optimization of SARS-Cov-2 Vaccine Distribution: an Evaluation of Second Dose Deferral Spacing of 6, 12, and 24 weeks
Bibliographic record
Abstract
Abstract Background Multiple recent studies have shown strong first dose vaccine efficacy for both Moderna mRNA-1273 and Pfizer/BioNTech BNT 162b2, which has stimulated discussion of maximizing initial population immunity during a time of vaccine shortage by using a deferred second dose strategy for these vaccines. Methods Our model examines the size of the effect of spacing of the second dose with 6, 12, and 24 week deferred spacing regimens relative to 3 week spacing. Results Deferring the second dose from 3 weeks to 6 weeks, 12 weeks, and 24 weeks shows progressive benefit to population immunity for any given time period, even with significant one dose efficacy decay. The benefits are influenced by vaccine supply per capita. Conclusion The longer the second dose is deferred the larger the benefit in initial population immunity, provided one dose efficacy does not significantly wane. Monitoring one dose efficacy duration from the UK or Quebec minimizes this risk, as the gathered data will help ensure the second dose is given at an optimal time. How this information is implemented should vary depending on the population and whether the goal is to optimally protect high risk groups or to increase total population immunity as quickly as possible. Benefits to deferring the second dose are influenced by the length of deferral, one dose efficacy, and vaccine supply per capita. The time to herd immunity could be shortened by 4 weeks with the implementation of a 12 week spacing regimen or 10 weeks with a 24 week spacing regimen.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".