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在多层疫情网络中促进预防行为的最优信息传播策略

Optimal information dissemination strategy to promote preventive behaviors in multilayer epidemic networks.

作者信息

Shakeri Heman, Sahneh Faryad Darabi, Scoglio Caterina, Poggi-Corradini Pietro, Preciado Victor M

机构信息

Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506-5204, United States.

出版信息

Math Biosci Eng. 2015 Jun;12(3):609-23. doi: 10.3934/mbe.2015.12.609.

Abstract

Launching a prevention campaign to contain the spread of infection requires substantial financial investments; therefore, a trade-off exists between suppressing the epidemic and containing costs. Information exchange among individuals can occur as physical contacts (e.g., word of mouth, gatherings), which provide inherent possibilities of disease transmission, and non-physical contacts (e.g., email, social networks), through which information can be transmitted but the infection cannot be transmitted. Contact network (CN) incorporates physical contacts, and the information dissemination network (IDN) represents non-physical contacts, thereby generating a multilayer network structure. Inherent differences between these two layers cause alerting through CN to be more effective but more expensive than IDN. The constraint for an epidemic to die out derived from a nonlinear Perron-Frobenius problem that was transformed into a semi-definite matrix inequality and served as a constraint for a convex optimization problem. This method guarantees a dying-out epidemic by choosing the best nodes for adopting preventive behaviors with minimum monetary resources. Various numerical simulations with network models and a real-world social network validate our method.

摘要

开展预防运动以控制感染传播需要大量资金投入;因此,在抑制疫情和控制成本之间存在权衡。个体之间的信息交流可以通过身体接触(如口碑传播、聚会)进行,而身体接触会带来疾病传播的内在可能性,也可以通过非身体接触(如电子邮件、社交网络)进行,通过非身体接触可以传播信息,但不会传播感染。接触网络(CN)包含身体接触,而信息传播网络(IDN)代表非身体接触,从而产生多层网络结构。这两层之间的固有差异导致通过接触网络发出警报比通过信息传播网络更有效,但成本更高。疫情消亡的约束源自一个非线性的佩龙 - 弗罗贝尼乌斯问题,该问题被转化为一个半定矩阵不等式,并作为一个凸优化问题的约束条件。这种方法通过选择采用预防行为的最佳节点并使用最少的资金资源来保证疫情的消亡。使用网络模型和真实世界社交网络进行的各种数值模拟验证了我们的方法。

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