1195569_Lincoln,T_2022.pdf (1.14 MB)
Download fileTaking a machine learning approach to optimize prediction of vaccine hesitancy in high income countries
journal contribution
posted on 2022-05-20, 05:02 authored by TM Lincoln, B Schlier, F Strakeljahn, BA Gaudiano, SH So, J Kingston, Eric MorrisEric Morris, L EllettUnderstanding 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.
Funding
Open Access funding enabled and organized by Projekt DEAL. There was no funding source for this study.
History
Publication Date
2022-12-01Journal
Scientific ReportsVolume
12Issue
1Article Number
ARTN 2055Pagination
12p.Publisher
Springer NatureISSN
2045-2322Rights Statement
© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/ licenses/by/4.0/Publisher DOI
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Keywords
Science & TechnologyMultidisciplinary SciencesScience & Technology - Other TopicsPSYCHOMETRIC PROPERTIESPOLITICAL TRUSTHERD-IMMUNITYPSYCHOSISORIGINSAdultAustraliaCOVID-19COVID-19 VaccinesCross-Sectional StudiesDeveloped CountriesFemaleGermanyHong KongHumansImmunization ProgramsMachine LearningMaleMass VaccinationMiddle AgedSARS-CoV-2United KingdomUnited StatesVaccination HesitancyPsychology not elsewhere classified