Analysing and forecasting COVID-19 vaccination - evidence from a Native American community in North Carolina, USA

Xin Zhang, Zhixin Kang, Guanlin Gao and Xinyan Shi

International Journal of Data Mining, Modelling and Management, 2025, vol. 17, issue 3, 245-271

Abstract: This study examines the determining factors of vaccination decisions for adults and children in a historical tribal region and evaluates various machine learning models in their predicting powers. COVID-19 vaccination data were investigated; though, the proposed method may be used for evaluating other vaccination data. We administrated a survey and collected cross-sectional data (e.g., socio-demographics, COVID-19 testing behaviours, vaccination status, and people's knowledge about, attitude toward, and belief in the vaccines), developed new features and built predicting models (e.g., random forest, neural network, and decision tree), and evaluated their performance against the benchmark logistic regression models. The results show that people, who tested more frequently, believed vaccination is a social responsibility, and were provided with paid leaves from employers are more likely to be fully vaccinated and vaccinate their children. Our results also show that not all machine learning models outperform the logistic regression model.

Keywords: COVID-19 vaccination intention; feature design and evaluation; vaccination forecasting; machine learning; Bayesian-correlation; model evaluation. (search for similar items in EconPapers)
Date: 2025
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