Impact of accelerating booster vaccination amidst Omicron surge in the United States
Bibliographic record
Abstract
Abstract COVID-19 infections driven by the Omicron variant are sweeping across the United States. Although early evidence suggests that the Omicron variant may cause less severe disease than previous variants, the explosive spread of infections threatens to drive hospitalizations and deaths to unprecedented high levels, swamping already overburdened hospitals. Booster vaccination appears to be effective at preventing severe illness and hospitalization. However, the pace of booster vaccination in the US has been slow despite the available infrastructure to administer doses at a much higher rate. We used an age-stratified, multi-variant agent-based model to project the reduction in COVID-related deaths and hospitalizations that could be achieved by accelerating the current daily pace of booster vaccination in the US. We found that doubling the rate of booster vaccination would prevent over 400,000 hospitalizations and 48,000 deaths. Tripling the booster vaccination rate would avert over 600,000 hospitalizations and save 70,000 lives during the first four months of 2022.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".