Bayesian network approach to develop generalisable predictive model for COVID-19 vaccine uptake
Bibliographic record
Abstract
Abstract The effectiveness of a vaccine depends on vaccine uptake, which is influenced by various factors, including vaccine hesitancy. Vaccine hesitancy is a complex socio-behavioral issue, influenced by misinformation, distrust in healthcare providers and government organizations, fear of side effects, and cultural or religious beliefs. To address this problem, AI models have been developed, but their global generalizability remains unclear. Therefore, this study aimed to identify global determinants of vaccine uptake and develop a generalizable machine learning model to predict individual-level vaccine uptake. The study used publicly available survey data from 23 countries and employed Bayesian networks and generalized mixed effects models to identify key determinants of vaccine uptake. The results showed that trust in the central government and vaccination restrictions for national and international travel were key determinants of vaccine uptake. A generalized mixed effects model achieved an AUC of 89% (SD=1%), precision of 90% (SD = 4%), and recall of 82% (SD=2%) on unseen testing data from new countries, demonstrating the model’s generalizability. The findings of this study can inform targeted interventions to improve vaccine uptake globally.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".