Influencing factors and dynamic changes of COVID-19 vaccine hesitancy in China: From the perspective of machine learning analysis
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 interpret...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Human Vaccines & Immunotherapeutics |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21645515.2025.2536898 |
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| author | Lei Li Hui Jing Yuqi Zhao Shenghua Wu Boyu Zhu |
| author_facet | Lei Li Hui Jing Yuqi Zhao Shenghua Wu Boyu Zhu |
| author_sort | Lei Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c8bddc0400f84d8db256498d5510f1b2 |
| institution | Kabale University |
| issn | 2164-5515 2164-554X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Human Vaccines & Immunotherapeutics |
| spelling | doaj-art-c8bddc0400f84d8db256498d5510f1b22025-08-20T03:31:54ZengTaylor & Francis GroupHuman Vaccines & Immunotherapeutics2164-55152164-554X2025-12-0121110.1080/21645515.2025.2536898Influencing factors and dynamic changes of COVID-19 vaccine hesitancy in China: From the perspective of machine learning analysisLei Li0Hui Jing1Yuqi Zhao2Shenghua Wu3Boyu Zhu4School of Law, Huaibei Normal University, Huaibei, ChinaSchool of Computer Science and Technology, Huaibei Normal University, Huaibei, ChinaCollege of Law, University of Birmingham, Birmingham, UKSchool of Law, Huaibei Normal University, Huaibei, ChinaSchool of Sociology, Wuhan University, Wuhan, ChinaExploring 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.https://www.tandfonline.com/doi/10.1080/21645515.2025.2536898COVID-19 vaccinevaccine hesitancydynamic transformationmachine learning |
| spellingShingle | Lei Li Hui Jing Yuqi Zhao Shenghua Wu Boyu Zhu Influencing factors and dynamic changes of COVID-19 vaccine hesitancy in China: From the perspective of machine learning analysis Human Vaccines & Immunotherapeutics COVID-19 vaccine vaccine hesitancy dynamic transformation machine learning |
| title | Influencing factors and dynamic changes of COVID-19 vaccine hesitancy in China: From the perspective of machine learning analysis |
| title_full | Influencing factors and dynamic changes of COVID-19 vaccine hesitancy in China: From the perspective of machine learning analysis |
| title_fullStr | Influencing factors and dynamic changes of COVID-19 vaccine hesitancy in China: From the perspective of machine learning analysis |
| title_full_unstemmed | Influencing factors and dynamic changes of COVID-19 vaccine hesitancy in China: From the perspective of machine learning analysis |
| title_short | Influencing factors and dynamic changes of COVID-19 vaccine hesitancy in China: From the perspective of machine learning analysis |
| title_sort | influencing factors and dynamic changes of covid 19 vaccine hesitancy in china from the perspective of machine learning analysis |
| topic | COVID-19 vaccine vaccine hesitancy dynamic transformation machine learning |
| url | https://www.tandfonline.com/doi/10.1080/21645515.2025.2536898 |
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