A Global Map of COVID-19 Vaccine Acceptance Rates per Country: An Updated Concise Narrative Review
Bibliographic record
Abstract
The delay or refusal of vaccination, which defines vaccine hesitancy, is a major challenge to successful control of COVID-19 epidemic. The huge number of publications addressing COVID-19 vaccine hesitancy necessitates periodic review to provide a concise summary of COVID-19 vaccine acceptance rates worldwide. In the current narrative review, data on COVID-19 vaccine acceptance rates were retrieved from surveys in 114 countries/territories. In East and Southern Africa (n = 9), the highest COVID-19 vaccine acceptance rate was reported in Ethiopia (92%), while the lowest rate was reported in Zimbabwe (50%). In West/Central Africa (n = 13), the highest rate was reported in Niger (93%), while the lowest rate was reported in Cameroon (15%). In Asia and the Pacific (n = 16), the highest rates were reported in Nepal and Vietnam (97%), while the lowest rate was reported in Hong Kong (42%). In Eastern Europe/Central Asia (n = 7), the highest rates were reported in Montenegro (69%) and Kazakhstan (64%), while the lowest rate was reported in Russia (30%). In Latin America and the Caribbean (n = 20), the highest rate was reported in Mexico (88%), while the lowest rate was reported in Haiti (43%). In the Middle East/North Africa (MENA, n = 22), the highest rate was reported in Tunisia (92%), while the lowest rate was reported in Iraq (13%). In Western/Central Europe and North America (n = 27), the highest rates were reported in Canada (91%) and Norway (89%), while the lowest rates were reported in Cyprus and Portugal (35%). COVID-19 vaccine acceptance rates ≥60% were seen in 72/114 countries/territories, compared to 42 countries/territories with rates between 13% and 59%. The phenomenon of COVID-19 vaccine hesitancy appeared more pronounced in the MENA, Europe and Central Asia, and Western/Central Africa. More studies are recommended in Africa, Eastern Europe and Central Asia to address intentions of the general public to get COVID-19 vaccination.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".