Prevention of seasonal influenza outbreak via healthcare insurance

Abstract

The outbreak of seasonal flu costs billions of dollars in health care utilization and lost productivity. Despite the effectiveness of vaccination and antiviral medications to prevent serious flu-related complications and slow down the spread of an influenza epidemic, only 52% of the U.S. population aged 6 months and older received flu vaccines in the 2019-20 flu season. In addition, a costly out-of-pocket expense results in fewer patients seeking treatment, leading to potential hospitalizations and even flu-related deaths. In this study, we develop an integrated healthcare insurance mechanism that optimizes two incentive policies, vaccination reward and cost-sharing, to alleviate the medical cost and disease burden while preventing the outbreak of seasonal influenza. We model the dynamic interaction between a single insurer and multiple insureds as a Stackelberg vaccination game; we then embed the game into an agent-based simulation to model the spread of flu in a population under different policies. Finally, we apply machine learning and simulation optimization to optimize healthcare incentive policies in a large-scale flu transmission simulation. Simulation results indicate that the proposed methodology efficiently identifies a set of good incentive policies under different scenarios of flu vaccine efficacy and reproduction numbers.