Optimizing vaccination campaign strategies considering societal characteristics

Area of Science:

• Epidemiology
• Public Health
• Behavioral Science

Background:

• Vaccine hesitancy remains a significant public health concern, impacting disease transmission dynamics.
• Understanding evolving vaccination opinions is crucial for effective public health interventions.
• Previous models often assumed static vaccination behavior, limiting real-world applicability.

Purpose of the Study:

• To explore the dynamic interplay between disease transmission, public opinion on vaccines, and targeted vaccination campaigns.
• To optimize vaccination strategies across diverse demographics based on varying levels of vaccine hesitancy.
• To provide actionable insights for real-world public health implementation.

Main Methods:

• A numerical experiment was conducted, calibrated to the COVID-19 epidemic in King County, WA, in 2023.
• A demographic-based networked compartmental model was employed to simulate disease spread and behavioral responses.
• Evolutionary game theory was utilized to model dynamic changes in vaccination attitudes.

Main Results:

• Vaccination campaigns show the highest effectiveness in populations with medium vaccine hesitancy, particularly targeting the 18-34 age group in densely populated areas.
• In low hesitancy settings, campaigns may be less critical, with resources better allocated to rural areas and the 0-17 age group.
• Campaigns are largely ineffective in high hesitancy societies; efforts should instead focus on mitigating vaccine risk perceptions.

Conclusions:

• Dynamic modeling of vaccination behavior offers more effective strategies than static approaches.
• Tailoring vaccination campaign focus (demographics, regions) based on hesitancy levels is essential for maximizing public health impact.
• Addressing vaccine risk perceptions is critical in high hesitancy environments where traditional campaigns fail.