Supply Chains and COVID-19 Vaccines: How Fast Can We Reach Herd Immunity?
Bibliographic record
Abstract
The COVID-19 epidemic continues to disrupt global society, but the advent of available vaccines in the Winter of 2021 meant that a return to some form of pre-pandemic “normal life” could be possible. With the supply disruptions and shortages through 2021 and 2022, the ability to both manufacture and distribute these remains important, especially with the rise of new variants and sub-variants that can evade earlier vaccines.US vaccine delivery strategies show the need to balance between focusing on delivery efficiency and delivery equity. Urban centers can focus on delivering mass numbers of vaccines quickly as these areas are often the epicenters for early stages of a pandemic, but rural areas need to focus on delivery equity over efficiency with sparsely-populated areas. Additional lessons include means to deal with vaccine-hesitant populations. This paper investigates how these changes can be implemented and the effect of current vaccine delivery with lessons for future pandemic preparation.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".