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Optimizing vaccination campaign strategies considering societal characteristics

Author

Listed:
  • Serin Lee

    (University of Washington)

  • Zelda B. Zabinsky

    (University of Washington)

  • Shan Liu

    (University of Washington)

Abstract

Vaccine hesitancy continues to be a public health challenge. This study explores the dynamic interplay between disease transmission, evolving vaccination opinions, and targeted vaccination campaigns. Using a numerical experiment calibrated to the COVID-19 epidemic in King County, WA, during 2023, we optimize vaccination campaigns across various demographics. Our findings suggest that vaccination campaigns are most effective in societies with medium vaccine hesitancy, with optimal outcomes achieved by focusing on the 18-34 age group in the most densely populated regions. In societies with low hesitancy, campaigns may be unnecessary, and resources should target rural areas and the 0-17 age range to maximize impact. In high hesitancy societies, campaigns are ineffective. In such cases, efforts should focus on reducing vaccine risk perceptions. This research advances the understanding of dynamic behavioral responses to vaccination campaigns through evolutionary game theory, moving beyond models that assume static vaccination behavior. By employing a demographic-based networked compartmental model, it derives actionable and interpretable campaign strategies, providing valuable guidance for real-world implementation.

Suggested Citation

  • Serin Lee & Zelda B. Zabinsky & Shan Liu, 2025. "Optimizing vaccination campaign strategies considering societal characteristics," Health Care Management Science, Springer, vol. 28(1), pages 84-98, March.
  • Handle: RePEc:kap:hcarem:v:28:y:2025:i:1:d:10.1007_s10729-025-09696-9
    DOI: 10.1007/s10729-025-09696-9
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    References listed on IDEAS

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    3. Jing Peng & Christophe Van den Bulte, 2024. "Participation vs. Effectiveness in Sponsored Tweet Campaigns: A Quality-Quantity Conundrum," Management Science, INFORMS, vol. 70(11), pages 7961-7983, November.
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