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. 2022 Sep 1;9(9):ofac446.
doi: 10.1093/ofid/ofac446. eCollection 2022 Sep.

Using a Cloud-Based Machine Learning Classification Tree Analysis to Understand the Demographic Characteristics Associated With COVID-19 Booster Vaccination Among Adults in the United States

Lu Meng  1   2 , Hannah E Fast  1   3 , Ryan Saelee  1   3 , Elizabeth Zell  1   4 , Bhavini Patel Murthy  1   3 , Neil Chandra Murthy  1   3 , Peng-Jun Lu  1   3 , Lauren Shaw  1   3 , LaTreace Harris  1   3 , Lynn Gibbs-Scharf  1   3 , Terence Chorba  1   5
Affiliations

Using a Cloud-Based Machine Learning Classification Tree Analysis to Understand the Demographic Characteristics Associated With COVID-19 Booster Vaccination Among Adults in the United States

Lu Meng et al. Open Forum Infect Dis. .

Abstract

A tree model identified adults age ≤34 years, Johnson & Johnson primary series recipients, people from racial/ethnic minority groups, residents of nonlarge metro areas, and those living in socially vulnerable communities in the South as less likely to be boosted. These findings can guide clinical/public health outreach toward specific subpopulations.

Keywords: COVID-19; COVID-19 vaccination; booster dose; coronavirus.

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Figures

Figure 1.
Figure 1.
Classification tree diagram depicting demographic characteristics associated with COVID-19 booster vaccination among adults completing the primary series before September 15, 2021, by social demographic factors, March 15, 2022, United States. Detailed information (eg, sample sizes, prediction rates, etc.) about the 17 end nodes is listed in Supplementary Table 1. Abbreviations: AI/AN, American Indian/Alaska Native; COVID-19, coronavirus disease 2019; J&J, Johnson & Johnson/Janssen; OPI, other Pacific Islander; SVI, Social Vulnerability Index.
See this image and copyright information in PMC

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