Document Type

Peer-Reviewed Article

Publication Date

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.

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.

DOI

10.1016/j.mlwa.2022.100408

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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