Document Type

Article

Publication Date

2025

Keywords

Clustering algorithm; COVID-19 pandemic; vaccine hesitancy; classification

Abstract

Reluctance or refusal to get vaccinated, commonly known as Vaccine Hesitancy (VH), poses a significant challenge to COVID-19 vaccination campaigns. Understanding the factors contributing to VH is essential for shaping effective public health strategies. This study proposes a novel framework for combining machine learning with publicly available data to generate a proxy metric that evaluates the dynamics of VH faster than the currently used survey methods. The metric is input to descriptive classification models that analyze a wide array of data, aiming to identify key factors associated with VH at the county level in the U.S. during the COVID-19 pandemic (i.e., January to October 2021). Both static and dynamic factors are considered. We use a Random Forest classifier that identifies political affiliation and Google search trends as the most significant factors influencing VH behavior. The model categorizes U.S. counties into five distinct clusters based on VH behavior. Cluster 1, with low VH, consists mainly of Democratic-leaning residents who, have the longest life expectancy, have a college degree, have the highest income per capita, and live in metropolitan areas. Cluster 5, with high VH, is predominantly Republican-leaning individuals in non-metropolitan areas. Individuals in Cluster 1 is more responsive to vaccination policies.

Comments

This work received support from the Open Access Publishing Fund, administered by the University of Arkansas Libraries, which has been instrumental in making this research openly accessible.

Additional Files
uiie_a_2301966_sm6573.pdf (256 kB)
Reproducibility Report
uiie_a_2301966_sm9213.pdf (1339 kB)
Supplemental Materials

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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