Dynamical behaviors for vaccination can suppress infectious disease - A game theoretical approach

Dynamical behaviors for vaccination can suppress infectious disease - A game theoretical approach
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DOI:
10.1016/j.chaos.2019.04.010
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发表时间:
2019-06-01
影响因子:
7.8
通讯作者:
Tanimoto, Jun
Tanimoto, Jun
中科院分区:
数学1区
文献类型:
--
作者:
Kabir, K. M. Ariful;Tanimoto, Jun

文献摘要

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为了避免感染,疫情爆发起着重要的作用,鼓励人们接种疫苗,并诱导行为改变。疾病发病率、疫苗接种和个人行为之间的相互作用发生在当地时间尺度上。在这里,我们分析了个人的行为在疾病-疫苗接种互动模型的基础上的进化博弈的方法,捕捉的想法,疫苗接种决定的疾病流行,也包括社会学习。当个体决定是否接种疫苗时,群体免疫的影响部分重要。一个人接种疫苗或被感染的可能性取决于有多少人接种疫苗。为了理解这种相互作用,四个策略更新规则:基于个人的风险评估(IB-RA),基于社会的风险评估(SB-RA),直接承诺(DC)和修改后的复制动力学(MRD)被认为是博弈论的方法,通过一个人如何从社会或邻居学习。本文的理论和研究结果为预防接种政策提供了一个新的视角,即提供及时学习和集体信息救济以减少感染,这是一个新的“预防接种博弈”。(C)2019爱思唯尔有限公司版权所有。
To avoid the infection, the epidemic outburst plays a significant role that encourages people to take vaccination and induce behavioral changes. The interplay between disease incidence, vaccine uptake and the behavior of individuals are taking place on the local time scale. Here, we analyze the individual's behavior in disease-vaccination interaction model based on the evolutionary game approach that captures the idea of vaccination decisions on disease prevalence that also include social learning. The effect of herd immunity is partly important when the individuals are deciding whether to take the vaccine or not. The possibility that an individual taking a vaccination or becoming infected depends upon how many other people are vaccinated. To apprehend this interplay, four strategy updating rules: individual based risk assessment (IB-RA), society based risk assessment (SB-RA), direct commitment (DC) and modified replicator dynamics (MRD) are contemplated for game theoretical approach by how one individual can learn from society or neighbors. The theory and findings of this paper provide a new perspective for vaccination taking policy in daily basis that provision of prompt learning with the collective information reliefs to reduce infection, which gives a new 'vaccination game' from other previous models. (C) 2019 Elsevier Ltd. All rights reserved.