Fair domestic allocation of monkeypox virus countermeasures
Bibliographic record
Abstract
Countermeasures for mpox (formerly known as monkeypox), primarily vaccines, have been in limited supply in many countries during outbreaks. Equitable allocation of scarce resources during public health emergencies is a complex challenge. Identifying the objectives and core values for the allocation of mpox countermeasures, using those values to provide guidance for priority groups and prioritisation tiers, and optimising allocation implementation are important. The fundamental values for the allocation of mpox countermeasures are: preventing death and illness; reducing the association between death or illness and unjust disparities; prioritising those who prevent harm or mitigate disparities; recognising contributions to combating an outbreak; and treating similar individuals similarly. Ethically and equitably marshalling available countermeasures requires articulating these fundamental objectives, identifying priority tiers, and recognising trade-offs between prioritising the people at the highest risk of infection and the people at the highest risk of harm if infected. These five values can provide guidance on preferable priority categories for a more ethically sound response and suggest methods for optimising allocation of countermeasures for mpox and other diseases for which countermeasures are in short supply. Properly marshalling available countermeasures will be crucial for future effective and equitable national responses to outbreaks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".