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网络上传染病流行时社交距离响应的最优控制与微分博弈解

Optimal control and differential game solutions for social distancing in response to epidemics of infectious diseases on networks.

作者信息

Dashtbali Mohammadali, Malek Alaeddin, Mirzaie Mehdi

机构信息

Department of Applied Mathematics, Faculty of Mathematical Sciences Tarbiat Modares University Tehran Iran.

出版信息

Optim Control Appl Methods. 2020 Nov-Dec;41(6):2149-2165. doi: 10.1002/oca.2650. Epub 2020 Aug 2.

Abstract

In this paper, the problem of social distancing in the spread of infectious diseases in the human network is extended by optimal control and differential game approaches. Hear, SEAIR model on simulation network is used. Total costs for both approaches are formulated as objective functions. SEAIR dynamics for group that contacts with individuals including susceptible, exposed, asymptomatically infected, symptomatically infected and improved or safe individuals is modeled. A novel random model including the concept of social distancing and relative risk of infection using Markov process is proposed. For each group, an aggregate investment is derived and computed using adjoint equations and maximum principle. Results show that for each group, investments in the differential game are less than investments in an optimal control approach. Although individuals' participation in investment for social distancing causes to reduce the epidemic cost, the epidemic cost according to the second approach is too much less than the first approach.

摘要

本文通过最优控制和微分博弈方法扩展了人类网络中传染病传播的社会距离问题。在此,使用了模拟网络上的SEAIR模型。两种方法的总成本都被制定为目标函数。对与包括易感者、暴露者、无症状感染者、有症状感染者以及康复或安全个体在内的个体接触的群体的SEAIR动态进行了建模。提出了一种使用马尔可夫过程的包含社会距离概念和相对感染风险的新型随机模型。对于每个群体,使用伴随方程和最大值原理推导并计算了总投资。结果表明,对于每个群体,微分博弈中的投资小于最优控制方法中的投资。尽管个体参与社会距离投资会降低疫情成本,但根据第二种方法计算的疫情成本比第一种方法少得多。

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