An alternative vaccine prioritization approach in response to COVID-19 pandemic
Bibliographic record
Abstract
Purpose The purpose of this study is to evaluate the impact of food access and other vulnerability measures on the COVID-19 progression to inform the public health decision-makers while setting priority rules for vaccine schedules. Design/methodology/approach In this paper, the authors used the Supplemental Nutrition Assistance Program (SNAP) data combined with the Centers for Disease Control and Prevention (CDC)’s social vulnerability score variables and diabetes and obesity prevalence in a set of models to assess the associations with the COVID-19 prevalence and case-fatality rates in the United States (US) counties. Using the case prevalence estimates provided by these models, the authors developed a COVID-19 vulnerability score. The COVID-19 vulnerability score prioritization is then compared with the pro-rata approach commonly used for vaccine distribution. Findings The study found that the population proportion residing in a food desert is positively correlated with the COVID-19 prevalence. Similarly, the population proportion registered to SNAP is positively correlated with the COVID-19 prevalence. The findings demonstrate that commonly used pro-rata vaccine allocation can overlook vulnerable communities, which can eventually create disease hot-spots. Practical implications The proposed methodology provides a rapid and effective vaccine prioritization scoring. However, this scoring can also be considered for other humanitarian programs such as food aid and rapid test distribution in response to the current and future pandemics. Originality/value Humanitarian logistics domain predominantly relies on equity measures, where each jurisdiction receives resources proportional to their population. This study provides a tool to rapidly identify and prioritize vulnerable communities while determining vaccination schedules.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".