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Identifying psychological predictors of SARS-CoV-2 vaccination: A machine learning study
Michael V Bronstein1, Erich Kummerfeld2, Angus MacDonald3
1Department of Psychiatry and Behavioral Sciences, University of Minnesota, MN, USA; Institute for Health Informatics, University of Minnesota, MN, USA.
Vaccine
|August 6, 2024
Summary
A machine learning model accurately predicts individuals who will remain unvaccinated against SARS-CoV-2. This tool can help target interventions to increase vaccine uptake among hesitant populations.
Area of Science:
- Public Health
- Computational Epidemiology
- Behavioral Science
Background:
- Addressing SARS-CoV-2 vaccine hesitancy is crucial.
- Predicting persistent unvaccinated individuals remains a challenge.
Purpose of the Study:
- Develop a machine learning model to prospectively predict SARS-CoV-2 vaccination status.
- Identify key predictors of vaccine refusal.
Main Methods:
- A random forest model was developed using baseline data from 325 unvaccinated individuals.
- Predictors included demographics, medical history, Health Belief Model constructs, and conspiracist ideation.
Main Results:
- The model accurately predicted vaccination status (AUC-PR=0.77).
- Key predictors included vaccine intentions, conspiracist ideation, perceived vaccine dangerousness, and influenza vaccination history.
Conclusions:
- The developed model can identify individuals likely to remain unvaccinated.
- Findings support interventions targeting vaccine hesitancy, misinformation, and perceived risks.
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