Global generalisability of AI-driven COVID-19 vaccination policies: a cross-sectional observational study

Objective To discover global determinants of vaccine uptake behavior and to develop a generalizable machine learning model to predict the vulnerability of vaccine uptake behavior at the individual level. Methodology 23135 Respondents across the 23 countries were interviewed for the survey questionnaire, after preprocessing and cleaning data, we performed Bayesian networks and generalized linear models to identify the key determinants of vaccine uptake. Markov Blankets obtained from the Bayesian networks were used to estimate the important predictors of the vaccine uptake. These variables were then used to build the models. To build generalizable models, we used country-wise data splitting. Model evaluation is assessed for the prediction performance on the new countries. We also developed income specific models cross validated within the income group. Results We found 16 important predictors of vaccine uptake using the Bayesian network and Markov Blanket approach. We found that the trust of the central government (Log-Odds 0.55\[0.25, 0.84\] (p= 0.0002)), Vaccination restriction for national and international travel (Log-Odds 0.4\[0.14, 0.65\] (p= 0.0034)) as the key determinants of Vaccine uptake. Our Generalized mixed effects model approach achieved an AUC of 89%, Precision 90% and Recall of 82% on the prediction task on new countries, thus, generalizing to new countries. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement NA ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Data is publicly availble and published by research paper