Related Experiment Video
Updated: Sep 13, 2025

Author Spotlight: Studying Host-Virus Interactions with Pseudotyped Viruses
Published on: November 21, 2023
Influencing factors and dynamic changes of COVID-19 vaccine hesitancy in China: From the perspective of machine
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.
Related Concept Videos
Steps in Outbreak Investigation
Single Nucleotide Polymorphisms-SNPs
Statistical Methods for Analyzing Epidemiological Data
Bias in Epidemiological Studies
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
Stereotype Content Model

