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Prevention as a Game-Theoretical Simulation in Bayesian Equilibria and Control through Real-World Data from Vaccination Monitoring

Humbsch Philipp

This study employs game theory in health economics to develop and test a decision-making model for citizens’ adherence to preventive measures, specifically analyzing the COVID-19 pandemic’s impact on European healthcare systems. The investigation delves into dynamic equilibria at decision nodes regarding preventive measures, utilizing Bayesian equilibria in mixed strategies. Theoretical foundations and simulations explore citizens’ cooperation or defection based on subjective payoffs, considering individual attributes and conditioning factors. Real-world data analysis, particularly focusing on COVID-19 vaccination rates, reveals disparities between simulated equilibria and observed outcomes. The study underscores the need to reduce vaccination barriers, emphasizing the continuous nature of vaccinations and suggests public awareness campaigns and improved appointment management to enhance follow-up vaccinations. It also raises concerns about the effectiveness of mandatory vaccination for diseases requiring frequent revaccination. The findings contribute to future discussions on vaccination policies and strategies to achieve herd immunity.