Beyond binary: a machine-learning classification of childhood COVID-19 vaccination intentions using behavioural data

Immunization is one of the most cost-effective tools for preventing infectious diseases. Yet, vaccine hesitancy, defined as a delayed acceptance or refusal of vaccination despite availability, has grown in recent years, threatening global public health efforts. This study investigates how socio-demographic and behavioural factors related to willingness to vaccinate children against COVID-19, moving beyond binary pro-/anti-vaccine classifications to explore a more nuanced spectrum of intentions. Using a large-scale survey conducted in summer 2021 among 5,552 adults (2,041 parents and 3,511 non-parents) in Italy and the UK, we applied supervised machine learning models (XGBoost, Random Forest, and Multinomial Logistic Regression) to identify population segments based on their willingness to vaccinate children against COVID-19. We emphasize the importance of intention-based segmentation by distinguishing between “unwilling”, “undecided,” and “willing” respondents, a classification that better reflects the continuum of vaccination intentions. Our findings, based on SHAP values analysis, show that friends’ opinion, the age of the child, and trust in vaccines are the strongest predictors of parental stances, with friends’ opinion emerging as the top factor across all models for parents. Overall, behavioural indicators played a key role in distinguishing between willingness groups. By integrating survey data with interpretable machine learning, this study highlights the importance of behavioural profiling and data collection for tailoring public health messages and targeting interventions to the most responsive segments of the population. While our empirical analysis is situated in the context of childhood COVID-19 vaccination, the framework has broader relevance for understanding parental decision-making and designing communication strategies in future vaccination campaigns.