Open Forum Infect Dis . 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

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Open Forum Infect Dis


. 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

• PMID: 36131845
• PMCID: PMC9452182
• DOI: 10.1093/ofid/ofac446

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.