A comparative analysis of supply chain factors impacting COVID-19 vaccine security in high-income countries (HICs) and low-income and middle-income countries (LMICs)
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic focused attention on the importance of vaccine security to national security. Demand for vaccines far exceeded supply when the first COVID-19 vaccines were released. Growing data suggest a non-perfect correlation among vaccine development, production, purchases, deliveries and vaccination rates. As such, the best approach to strengthening vaccine security remains unclear. In this study, we use an operations research/operations management framework to characterise the relationship between vaccine security and key supply chain predictor variables in high-income countries (HICs) and low-income and middle-income countries (LMICs). METHODS: We performed a comparative analysis of vaccine security against eight supply chain variables in a purposive sample of five HICs and five LMICs during the early phase of the pandemic (31 March 2021 and 30 April 2021). All data were obtained from publicly available databases. We used descriptive statistics to characterise our data, basic statistics to compare data and scatter plots to visualise relationships. RESULTS: HICs, with Canada, Israel and Japan being frequent outliers, and within LMICs, with India standing out. CONCLUSION: Our data suggest a stronger relationship between vaccine security and 'downstream' supply chain variables compared with 'upstream' variables. However, multiple outliers and the lack of an even stronger relationship suggests that there is no magic bullet for vaccine security. To boost vaccine resilience, countries must be well governed and strategically reinforce deficient aspects of their supply chains. Modest strength in multiple domains may be the best approach to counteracting the effect of an unfamiliar, novel pathogen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".