Taking a machine learning approach to optimize prediction of vaccine hesitancy in high income countries
journal contributionposted on 20.05.2022, 05:02 authored by TM Lincoln, B Schlier, F Strakeljahn, BA Gaudiano, SH So, J Kingston, Eric MorrisEric Morris, L Ellett
Understanding factors driving vaccine hesitancy is crucial to vaccination success. We surveyed adults (N = 2510) from February to March 2021 across five sites (Australia = 502, Germany = 516, Hong Kong = 445, UK = 512, USA = 535) using a cross-sectional design and stratified quota sampling for age, sex, and education. We assessed willingness to take a vaccine and a comprehensive set of putative predictors. Predictive power was analysed with a machine learning algorithm. Only 57.4% of the participants indicated that they would definitely or probably get vaccinated. A parsimonious machine learning model could identify vaccine hesitancy with high accuracy (i.e. 82% sensitivity and 79–82% specificity) using 12 variables only. The most relevant predictors were vaccination conspiracy beliefs, various paranoid concerns related to the pandemic, a general conspiracy mentality, COVID anxiety, high perceived risk of infection, low perceived social rank, lower age, lower income, and higher population density. Campaigns seeking to increase vaccine uptake need to take mistrust as the main driver of vaccine hesitancy into account.
Open Access funding enabled and organized by Projekt DEAL. There was no funding source for this study.
Article NumberARTN 2055
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Science & TechnologyMultidisciplinary SciencesScience & Technology - Other TopicsPSYCHOMETRIC PROPERTIESPOLITICAL TRUSTHERD-IMMUNITYPSYCHOSISORIGINSAdultAustraliaCOVID-19COVID-19 VaccinesCross-Sectional StudiesDeveloped CountriesFemaleGermanyHong KongHumansImmunization ProgramsMachine LearningMaleMass VaccinationMiddle AgedSARS-CoV-2United KingdomUnited StatesVaccination Hesitancy