Optimizing vaccine logistics: a taxonomy and narrative review
Bibliographic record
Abstract
The topic of vaccine logistics, which entails the sourcing, storage, distribution, and administration of vaccines, has received considerable attention from researchers and policymakers in the last few decades. Interest in this topic has particularly increased after the COVID-19 pandemic, highlighting the importance of vaccine logistics and the need for efficient and effective vaccine distribution systems to contain the pandemic. Despite the substantial recent growth in the knowledge of various aspects of vaccine logistics, the literature is still vastly dispersed and inadequately analyzed. This paper offers a comprehensive narrative review of the vaccine logistics literature, delving into 82 articles centered around optimization models. We construct a taxonomy to categorize the existing body of literature and provide a systematic analysis of the scope, methodologies, and objectives that define the current research landscape. Our review aspires to contribute to a deeper understanding of vaccine logistics and optimization by synthesizing and organizing this wealth of information. The insights from this analysis provide valuable guidance for researchers, policymakers, and practitioners in the field. Ultimately, we aim to enhance the collective knowledge base and inform future endeavors in vaccine distribution and optimization strategies.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".