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Primary Factors Influencing the Decision to Vaccinate against COVID-19 in the United States: A Pre-Vaccine Analysis

Author

Listed:
  • Serkan Varol

    (Department of Engineering Management, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA)

  • Serkan Catma

    (Department of Business Administration, University of South Carolina Beaufort, Bluffton, SC 29902, USA)

  • Diana Reindl

    (Department of Nursing and Health Professions Business, University of South Carolina Beaufort, Bluffton, SC 29902, USA)

  • Elizabeth Serieux

    (Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98105, USA)

Abstract

Because vaccine hesitancy is a dynamic trait, it is critical to identify and compare the contributing factors at the different stages of a pandemic. The prediction of vaccine decision making and the interpretation of the analytical relationships among variables that encompass public perceptions and attitudes towards the COVID-19 pandemic have been extensively limited to the studies conducted after the administration of the first FDA-approved vaccine in December of 2020. In order to fill the gap in the literature, we used six predictive models and identified the most important factors, via Gini importance measures, that contribute to the prediction of COVID-19 vaccine acceptors and refusers using a nationwide survey that was administered in November 2020, before the widespread use of COVID-19 vaccines. Concerns about (re)contracting COVID-19 and opinions regarding mandatory face covering were identified as the most important predictors of vaccine decision making. By investigating the vaccine acceptors and refusers before the introduction of COVID-19 vaccines, we can help public health officials design and deliver individually tailored and dynamic vaccination programs that can increase the overall vaccine uptake.

Suggested Citation

  • Serkan Varol & Serkan Catma & Diana Reindl & Elizabeth Serieux, 2022. "Primary Factors Influencing the Decision to Vaccinate against COVID-19 in the United States: A Pre-Vaccine Analysis," IJERPH, MDPI, vol. 19(3), pages 1-11, January.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1026-:d:727035
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    References listed on IDEAS

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    1. Ashley I Heinson & Rob M Ewing & John W Holloway & Christopher H Woelk & Mahesan Niranjan, 2019. "An evaluation of different classification algorithms for protein sequence-based reverse vaccinology prediction," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-13, December.
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