COVAX and COVID‐19 Vaccine Inequity: A case study of G‐20 and African Union
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As the world has a history of vaccine nationalism, especially during the 2009 Swine flu pandemic, the COVAX alliance, a globally collaborated mechanism, was created by World Health Organization (WHO), GAVI, and UNICEF to address the inequity of COVID-19 vaccines. One of the primary aims of this alliance was to deliver vaccines to low- and middle-income countries (LMICs), which otherwise have less or no capacity to access vaccines from the open market. It is crucial to explore the contribution of COVAX in bridging the gap in equity, accessibility, and affordability of COVID-19 vaccines between high- and low-income countries (LICs). We selected Group 20 (G20) COVAX participants and the African Union (AU) as case studies to estimate these gaps. The bilateral purchase data shows that by December 2021, the G20 countries had vaccines more than double their population, whereas the AU could procure only about one fifth (19%) of their population. Out of 52 AU countries whose data was available, only 21 of them could strike a bilateral deal with vaccine manufacturers. Even after COVAX delivery, the share of the population that could be vaccinated in AU was just 36.8%, less than the target of WHO (40%) for December 2021. It was found that the COVAX alliance worked better than the open market competition for LMICs and LICs. The cost of vaccinating 20% of the population was 0.7% of the current health expenditure for G20 countries, whereas AU countries had to spend 5.5%. COVAX bears more cost (1%-3%) for AU countries than G20 countries (less than 1%). COVAX made COVID-19 vaccines more affordable and accessible to these countries. However, LICs were disproportionately affected even with the COVAX Facility mechanism owing to their lack of vaccine deployment infrastructure.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it