Using machine learning models to predict vaccine hesitancy: a showcase of COVID-19 vaccine hesitancy in rural populations during the pandemic

Yang Ge, Abeera Zahid, Rishitha Kuchur, Leonardo Martinez, Bo Wang, Lei Zhang, Sermin Aras, Pooja Raynee, Aimee Dike, Cali Navarro, Chelsey Lawrick, Tammy Greer, Felix Twum, Ye Shen, June Gipson, Jennifer Lemacks,
Using machine learning models to predict vaccine hesitancy: a showcase of COVID-19 vaccine hesitancy in rural populations during the pandemic,
Vaccine,
Volume 66,
2025,
127799,
ISSN 0264-410X,
https://doi.org/10.1016/j.vaccine.2025.127799.
(https://www.sciencedirect.com/science/article/pii/S0264410X25010965)
Abstract: Understanding vaccine hesitancy is a critical public health challenge, yet traditional statistical methods often fail to capture the complex drivers behind it. This study uses COVID-19 vaccine hesitancy in a rural population as a case study to demonstrate a more powerful and interpretable machine learning workflow. We compared seven models and found that non-linear approaches significantly outperformed logistic regression in predictive accuracy. Interpretation of the best-performing model identified vaccine safety perceptions as the most important predictor. This approach revealed nuanced, non-linear relationships with feature importance and partial dependence plots. This work serves as a practical guide for researchers, showing how a machine learning framework provides not only more accurate predictions but also a richer, more actionable understanding of complex human behaviors to better inform public policy.
Keywords: Vaccine hesitancy; COVID-19; Machine learning; Rural population