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Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables:

Nicole L Vike1, Sumra Bari1, Leandros Stefanopoulos2,3

  • 1Department of Computer Science, University of Cincinnati, Cincinnati, OH, United States.

JMIR Public Health and Surveillance
|February 5, 2024
PubMed
Summary

Understanding judgment psychology is key to increasing COVID-19 vaccine uptake. Machine learning models show judgment variables significantly predict vaccination choices, informing public health strategies.

Keywords:
aversionbalanced random forestbehavioral economicscognitive sciencejudgmentmachine learningmediationmobile phonemoderationrelative preference theoryrewardsmartphone

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Area of Science:

  • Cognitive Science
  • Behavioral Economics
  • Machine Learning

Background:

  • Many individuals did not receive COVID-19 vaccines despite mandates.
  • Psychological factors, specifically reward and aversion judgments, influence vaccination decisions.
  • Previous research has not integrated cognitive science judgment variables with machine learning to predict vaccine uptake.

Purpose of the Study:

  • To assess the predictive capability of judgment variables for COVID-19 vaccine uptake using machine learning.
  • To identify key judgment profiles influencing vaccination decisions.

Main Methods:

  • Surveyed 3476 US adults on demographics, vaccine uptake, and precautions.
  • Utilized a picture-rating task to quantify liking and disliking, modeling judgment features.
  • Employed random forest, balanced random forest (BRF), and logistic regression to predict vaccine uptake.

Main Results:

  • Demographics and most judgment variables differed significantly between vaccinated and unvaccinated groups.
  • Balanced Random Forest (BRF) demonstrated superior performance with high precision (87.8%) and AUROC (79%).
  • Judgment variables constituted 63-75% of feature importance in prediction models; age, income, and education mediated these relationships.

Conclusions:

  • Judgment variables are crucial for understanding and predicting vaccine choice.
  • Tailoring vaccine education and messaging to specific judgment profiles can enhance uptake.
  • The methodology can support public health preparedness by identifying at-risk populations for targeted interventions.