โก Quick Summary
This study utilized a Gradient Boosting Decision Tree (GBDT) model to analyze factors influencing COVID-19 vaccine hesitancy in China, revealing that key determinants evolve between primary vaccinations and booster shots. The findings emphasize the need for adaptable vaccination strategies that consider changing public perceptions and decision-making processes.
๐ Key Details
- ๐ Dataset: Survey data from China
- ๐งฉ Features analyzed: Social norms, vaccine knowledge, anticipated regret, age, and more
- โ๏ธ Technology: Gradient Boosting Decision Tree (GBDT) with SHAP interpretability analysis
- ๐ Key findings: Factors influencing hesitancy differ between primary series and booster shots
๐ Key Takeaways
- ๐ Primary vaccinations are influenced by social norms, vaccine knowledge, and perceived safety.
- ๐ก Booster shots show a shift in focus towards personal experiences and vaccine effectiveness.
- ๐ฉโ๐ฌ Age plays a critical role in both primary and booster vaccination hesitancy.
- ๐ The decision-making process is evolving from emotion-driven to cognition-driven.
- ๐ Public health strategies must adapt to these dynamic changes in hesitancy factors.
- ๐ Timely adjustments in vaccination strategies are essential to address public concerns effectively.
- ๐ The study highlights the importance of comprehensive consideration of multiple factors in vaccination campaigns.
๐ Background
Understanding vaccine hesitancy is crucial for public health, especially in the context of the ongoing COVID-19 pandemic. Vaccine hesitancy can lead to lower vaccination rates, which in turn can exacerbate public health crises. By identifying the factors that contribute to hesitancy, health authorities can develop targeted strategies to encourage vaccination and improve community health outcomes.
๐๏ธ Study
This research was conducted using survey data collected in China, where the authors employed a Gradient Boosting Decision Tree (GBDT) model to analyze the influencing factors of COVID-19 vaccine hesitancy. The study also utilized SHAP interpretability analysis to provide insights into the significance of various factors affecting public attitudes towards vaccination.
๐ Results
The analysis revealed that for the primary series of COVID-19 vaccines, critical factors included social norms, vaccine knowledge, and perceived safety. In contrast, for booster shots, the focus shifted to personal vaccination experiences and perceived effectiveness. This indicates a dynamic evolution in vaccine hesitancy, highlighting the need for ongoing research and adaptation of public health strategies.
๐ Impact and Implications
The findings of this study have significant implications for public health policy and vaccination campaigns. By recognizing the evolving nature of vaccine hesitancy, health authorities can tailor their strategies to address the specific concerns of different demographic groups. This adaptability is essential for improving vaccination rates and ultimately controlling the spread of COVID-19.
๐ฎ Conclusion
This research underscores the importance of understanding the factors influencing COVID-19 vaccine hesitancy and the necessity for adaptable public health strategies. As the landscape of vaccine acceptance continues to change, it is vital for health authorities to remain responsive to public concerns and to incorporate these insights into their vaccination campaigns. The future of public health depends on our ability to navigate these complexities effectively.
๐ฌ Your comments
What are your thoughts on the evolving factors of vaccine hesitancy? How can we better address these challenges in public health? ๐ฌ Share your insights in the comments below or connect with us on social media:
Influencing factors and dynamic changes of COVID-19 vaccine hesitancy in China: From the perspective of machine learning analysis.
Abstract
Exploring the influencing factors of COVID-19 vaccine hesitancy and summarizing countermeasures is of great significance for effectively addressing potential public health crises. Based on survey data from China, we employed a Gradient Boosting Decision Tree (GBDT) model and conducted SHAP interpretability analysis. The results show that in the primary series of COVID-19 vaccines, the important factors include social norms, vaccine knowledge, anticipated regret, age, vaccine safety, social responsibility, education level, religious belief, vaccine effectiveness, and perceived severity. While for booster shots, the important variables include age, vaccination experience, vaccine knowledge, vaccine effectiveness, gender, perceived severity, concerns about the epidemic, social norms, anticipated regret, and sense of social responsibility. The differences in the composition and significance of these core predictive factors suggest that COVID-19 vaccine hesitancy is dynamically evolving. This pattern of evolution is manifested as a shift in the decision – making basis from social norms to subjective experiences, in the focus of vaccines from safety – first to effectiveness – priority, and in the decision – making mechanism from emotion – dominated to cognition – driven. The research findings inspire us that when formulating vaccination strategies, multiple factors need to be comprehensively considered. Moreover, strategies should be adjusted in a timely manner according to changes in the vaccination stages to align with the shift in public concerns and decision – making mechanisms.
Author: [‘Li L’, ‘Jing H’, ‘Zhao Y’, ‘Wu S’, ‘Zhu B’]
Journal: Hum Vaccin Immunother
Citation: Li L, et al. Influencing factors and dynamic changes of COVID-19 vaccine hesitancy in China: From the perspective of machine learning analysis. Influencing factors and dynamic changes of COVID-19 vaccine hesitancy in China: From the perspective of machine learning analysis. 2025; 21:2536898. doi: 10.1080/21645515.2025.2536898