Prevention of seasonal influenza outbreak via healthcare insurance
Prevention of seasonal influenza outbreak via healthcare insurance
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DOI：
10.1080/24725579.2022.2145393
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发表时间：
2023-10-02
影响因子：
--
通讯作者：
Liu，Shan
中科院分区：
文献类型：
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作者：
Ho，Ting-Yu;Zabinsky，Zelda B.;Liu，Shan
季节性流感的爆发使卫生保健利用率和生产力损失了数十亿美元。尽管疫苗接种和抗病毒药物可以有效预防严重的流感相关并发症并减缓流感疫情的传播，但在2019-20流感季节，只有52%的6个月及以上的美国人口接种了流感疫苗。此外，昂贵的自付费用导致寻求治疗的患者减少，导致潜在的住院治疗，甚至与流感相关的死亡。本研究提出一套整合的医疗保险机制，以疫苗接种奖励与费用分担两项激励政策为最佳化，在预防季节性流感爆发的同时，减轻医疗成本与疾病负担。我们模拟一个单一的保险公司和多个被保险人之间的动态互动作为Stackelberg疫苗接种游戏，然后我们嵌入到一个基于代理的模拟模型的流感在不同的政策下的人口中的传播的游戏。最后，我们应用机器学习和仿真优化来优化大规模流感传播模拟中的医疗激励政策。仿真结果表明，所提出的方法有效地确定了一组良好的激励政策，在不同的情况下，流感疫苗的效力和繁殖数量。
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.