Equitable Allocation of Scarce Resources During the Covid-19 Pandemic: A Case Study for Convalescent Plasma Distribution
Bibliographic record
Abstract
Resource planning during pandemics presents many challenges and equitable decisions about resource allocation must be made. There is no standard definition of equity. Robust mathematical formulations can require a lot of data. In a novel pandemic there is limited historical information available to inform decisions. Decision makers can look to define equity through population proportions (pro-rata). This notion of equity is readily implementable. We present a practical framework for an equitable allocation of scarce resources using population proportions, disease demographics, and resource utilization. We assess our framework using a stochastic simulation model, calibrated to COVID-19 case data, in a case study for convalescent plasma distribution in the context of the clinical trial CONCOR-1. We show that pro-rata resource allocation can be inequitable and that decision makers can consider readily available information, such as resource utilization and case data, to inform equity and proactively manage scarce resources during a pandemic.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".