Taking a machine learning approach to optimize prediction of vaccine hesitancy in high income countries.

ABSTRACT: 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, va

SUBMITTER: Lincoln TM 

PROVIDER: S-EPMC8827083 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature