Equitable global allocation of monkeypox vaccines
Bibliographic record
Abstract
With the world grappling with continued spread of monkeypox internationally, vaccines play a crucial role in mitigating the harms from infection and preventing spread. However, countries with the greatest need - particularly historically endemic countries with the highest monkeypox case-fatality rates - are not able to acquire scarce vaccines. This is unjust, and requires rectification through equitable allocation of vaccines globally. We propose applying the Fair Priority Model for such allocation, which emphasizes three key principles: 1) preventing harm; 2) prioritizing the disadvantaged; and 3) treating people with equal moral concern. Post-exposure prophylaxis (PEPV) has the most potential to mitigate harm, and so ensuring countries have sufficient supply for PEPV should be the first priority. And historically endemic countries, which face disadvantages that compound potential harms from monkeypox, should be the first recipients of such vaccines. Once sufficient supply is allocated for countries to apply PEPV, global allocation could move on to pre-exposure prophylaxis (PrEP), again prioritizing historically endemic countries first before distribution to the rest of the global community, based on projected number of cases and vulnerability to harm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".