Open Forum Infect Dis . Using a Cloud-Based Machine Learning Classification Tree Analysis to Understand the Demographic Characteristics Associated W

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Open Forum Infect Dis


. 2022 Sep 1;9(9)fac446.
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[SUP] 1 2 [/SUP], Hannah E Fast[SUP] 1 3 [/SUP], Ryan Saelee[SUP] 1 3 [/SUP], Elizabeth Zell[SUP] 1 4 [/SUP], Bhavini Patel Murthy[SUP] 1 3 [/SUP], Neil Chandra Murthy[SUP] 1 3 [/SUP], Peng-Jun Lu[SUP] 1 3 [/SUP], Lauren Shaw[SUP] 1 3 [/SUP], LaTreace Harris[SUP] 1 3 [/SUP], Lynn Gibbs-Scharf[SUP] 1 3 [/SUP], Terence Chorba[SUP] 1 5 [/SUP]



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.