Influencing factors and dynamic changes of COVID-19 vaccine hesitancy in China: From the perspective of machine learning analysis
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Discussion
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Implication
Analysis of vaccine hesitancy identifies key factors and dynamic shifts in attitudes, suggesting timely adjustments in vaccination strategies.
Key Points
- COVID-19 vaccine hesitancy evolves as key factors shift from social norms to personal experiences, suggesting nuanced public responses.
- The Gradient Boosting Decision Tree model revealed age and vaccine knowledge as pivotal factors affecting both primary and booster shots.
- SHAP analysis highlighted that perceived severity and social responsibility are important in understanding vaccine hesitancy dynamics.
- Timely adjustments in vaccination strategies are essential to align with evolving public concerns and decision-making processes.
Cite This Study
Li et al. (2025) studied this question.