Towards a predictive model of COVID-19 vaccine hesitancy among American adults

Designing effective public health campaigns to combat COVID-19 vaccine hesitancy requires an understanding of i) who the vaccine hesitant population is, and ii) the determinants of said population's hesitancy. While researchers have identified a number of variables associated with COVID-19 vaccine hesitancy that could inform such campaigns, little is known about the cumulative or relative predictive power of these factors. In this article, we employ a machine learning model to analyze online survey data collected from 3353 respondents. The model incorporates an array of variables that have been shown to impact vaccine hesitancy, allowing us to i) test how well we can predict vaccine hesitancy, and ii) compare the relative predictive impact of each covariate. The model allows us to correctly classify individuals that are vaccine acceptant with 97% accuracy, and those that are vaccine hesitant with 72% accuracy. Trust in and knowledge about vaccines is, by far, the strongest predictor of vaccination choice. While our results demonstrate that public health campaigns designed to increase vaccination rates must find a way to increase public trust in COVID-19 vaccines, our results cannot speak to the malleability of such beliefs, nor how to enhance trust.