A Prediction Model for Identifying Seasonal Influenza Vaccination Uptake Among Children in Wuxi, China: Prospective Observational Study

BACKGROUND: Predicting vaccination behaviors accurately could provide insights for health care professionals to develop targeted interventions. OBJECTIVE: The aim of this study was to develop predictive models for influenza vaccination behavior among children in China. METHODS: We obtained data from a prospective observational study in Wuxi, eastern China. The predicted outcome was individual-level vaccine uptake and covariates included sociodemographics of the child and parent, parental vaccine hesitancy, perceptions of convenience to the clinic, satisfaction with clinic services, and willingness to vaccinate. Bayesian networks, logistic regression, least absolute shrinkage and selection operator (LASSO) regression, support vector machine (SVM), naive Bayes (NB), random forest (RF), and decision tree classifiers were used to construct prediction models. Various performance metrics, including area under the receiver operating characteristic curve (AUC), were used to evaluate the predictive performance of the different models. Receiver operating characteristic curves and calibration plots were used to assess model performance. RESULTS: A total of 2383 participants were included in the study; 83.2% of these children (n=1982) were