Document Type
Peer-Reviewed Article
Publication Date
12-15-2022
Abstract
In this study, we examine state-level features and policies that are most important in achieving a threshold level vaccination rate to curve the effects of the COVID-19 pandemic. We employ CHAID, a decision tree algorithm, on three different model specifications to answer this question based on a dataset that includes all the states in the United States. Workplace travel emerges as the most important predictor; however, the governors’ political affiliation (PA) replaces it in a more conservative feature set that includes economic features and the growth rate of COVID-19 cases. We also employ several alternative algorithms as a robustness check. Results from these checks confirm our original findings regarding workplace travels and political affiliation. The accuracy under different model specifications ranges from 80%–88%, whereas the sensitivity is between 92.5%–100%. Our findings provide actionable policy insights to increase vaccination rates and combat the COVID-19 pandemic.
DOI
10.1016/j.mlwa.2022.100408
Recommended Citation
Osman, S. M. I., & Sabit, A. (2022). Predictors of COVID-19 vaccination rate in USA: A machine learning approach. Machine Learning with Applications, 10, 100408. Doi: 10.1016/j.mlwa.2022.100408
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Included in
Categorical Data Analysis Commons, Influenza Virus Vaccines Commons, Management Sciences and Quantitative Methods Commons
Comments
PMID: 36128042
Available online 16 September 2022.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Version posted is the In Press, Journal Pre-proof.