A protocol for modeling the factors influencing the deployment of the COVID-19 vaccine across African countries
Bibliographic record
Abstract
Evidence fails to capture disparities amongst African countries in terms of the measure of administered COVID-19 vaccine doses per 100 people. Assessment of data on doses secured, administered, and supplied was undertaken to investigate quantitative measures that impacted COVID-19 vaccine deployment, thereby emphasizing distribution and supply indicators. We employ a full linear regression to identify independent variables that have an impact on vaccination rates, including macroeconomic indicators such as World Bank Income Classification, Gross Domestic Product (GDP) per capita and various indices such as Health Access and Quality Index, Human Development Index, Global Peace Index, Education Index, Political Stability Index and Government Effectiveness. This analysis aims to construct a statistical model utilizing regression analysis to identify key drivers of COVID-19 vaccine deployment in Africa and offer insights into vaccination disparities in the continent. Recognizing the global importance of achieving high vaccination rates, the study sheds light on specific challenges faced by individual countries within Africa, thereby emphasizing the need for tailored efforts. Beyond COVID-19, the research contributes to understanding the relationship between vaccination rates and social indicators that, potentially impact broader public health concerns and global vaccination programs. This study provides a foundation for informed policymaking to enhance vaccine accessibility, inform targeted programs, and improve individual health systems, thereby addressing broader implications for global health.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.089 | 0.008 |
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".