Centrality-based epidemic control in complex social networks
Centrality-based epidemic control in complex social networks
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DOI：
10.1007/s13278-020-00638-7
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发表时间：
2020-05-22
影响因子：
2.8
通讯作者：
Khan, Usman A.
中科院分区：
文献类型：
--
作者：
Doostmohammadian, Mohammadreza;Rabiee, Hamid R.;Khan, Usman A.
网络科学和控制领域的最新进展显示了理解和分析流行病过程的重要前景。控制界和流行病学研究界使用的一个众所周知的研究流行病过程的模型是易感-感染-易感（SIS）动力学，用于模拟疾病/病毒在感染者和易感者的接触网络上的传播。SIS模型有两个亚稳态：一个称为地方病平衡，另一个称为无病或健康状态平衡。控制理论提供了设计控制行动（分配治疗或疫苗接种资源）的工具，以实现和稳定无病均衡。然而，控制行动往往是为整个社区设计的。基于图论控制的思想，本文旨在研究将治疗资源分配给目标群体而不是整个社区。该目标群体是基于不同类型随机网络中的中心性排名来选择的。我们的研究结果表明，特定的图形属性涉及的流行病控制。特别是，我们表明（1）聚类系数和（2）网络的度分布是有效的，在选择这些目标群体的流行病控制。
Recent progress in the areas of network science and control has shown a significant promise in understanding and analyzing epidemic processes. A well-known model to study epidemics processes used by both control and epidemiological research communities is the susceptible-infected-susceptible (SIS) dynamics to model the spread of disease/viruses over contact networks of infected and susceptible individuals. The SIS model has two metastable equilibria: one is called the endemic equilibrium and the other is known as the disease-free or healthy-state equilibrium. Control theory provides the tools to design control actions (allocating curing or vaccination resources) in order to achieve and stabilize the disease-free equilibrium. However, the control actions are often designed for the entire community. Based on the ideas developed in graph-theoretic control, this paper aims to study allocating curing resources to a target group instead of the entire community. This target group is selected based on centrality rank in different types of random networks. Our results show that specific graph properties are involved in the epidemic control. In particular, we show that (1) the clustering-coefficient and (2) the degree distribution of the network are effective in the selection of these target groups for epidemic control.