ArticleScientific reports2022
Taking a machine learning approach to optimize prediction of vaccine hesitancy in high income countries.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Bayesian uncertainty quantification to identify population level vaccine hesitancy behaviours.PloS one · 2026Article
- Developing and validating machine learning models to predict vaccine hesitancy and literacy among adults in the United States.Frontiers in public health · 2026Article
- Beyond binary: a machine-learning classification of childhood COVID-19 vaccination intentions using behavioural data.Population health metrics · 2025Article
- Evaluating socioeconomic influences on Nipah virus vaccination decisions in Bangladesh through machine learning.Journal of health, population, and nutrition · 2025Article
- Using machine learning to predict persecutory beliefs based on aetiological models of delusions identified in a systematic literature search.Communications psychology · 2025Article
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- Context matters: How to research vaccine attitudes and uptake after the COVID-19 crisis.Human vaccines & immunotherapeutics · 2024Article
- Conspiracy narratives and vaccine hesitancy: a scoping review of prevalence, impact, and interventions.BMC public health · 2024Article
- Evaluating vaccination timing, hesitancy and effectiveness to prevent future outbreaks: insights from COVID-19 modelling and transmission dynamics.Royal Society open science · 2024Article
- Cross-cultural validation of the revised Green et al., paranoid thoughts scale.Psychological medicine · 2024Article
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- A Snapshot of COVID-19 Vaccine Discourse Related to Ethnic Minority Communities in the United Kingdom Between January and April 2022: Mixed Methods Analysis.JMIR formative research · 2024Article
- The relationship between risk perception, anxiety and paranoia - A predictive model in a community sample.Journal of mood and anxiety disorders · 2024Article
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- Differentiating paranoia and conspiracy mentality using a network approach.Scientific reports · 2023Article
- Paranoid Thinking as a Function of Minority Group Status and Intersectionality: An International Examination of the Role of Negative Beliefs.Schizophrenia bulletin · 2023Article
- Predictors of COVID-19 vaccination rate in USA: A machine learning approach.Machine learning with applications · 2022Article
- A machine learning algorithm to analyse the effects of vaccination on COVID-19 mortality.Epidemiology and infection · 2022Article
- Attitudes toward the pandemic and COVID-19 vaccination intention among German university students and the general population: Results from two cross-sectional surveys.Frontiers in public health · 2022Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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, 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.