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Containing epidemics in a local cluster via antidote distribution and partial quarantine
Physical Review E ( IF 2.4 ) Pub Date : 2021-09-17 , DOI: 10.1103/physreve.104.034307
Zhenqi Lu 1 , Johan Wahlström 2 , Arye Nehorai 1
Affiliation  

The study of spreading phenomena in networks, in particular the spread of disease, has attracted considerable interest in the network science research community. In this paper, we show that the outbreak of an epidemic can be effectively contained and suppressed in a small subnetwork by a combination of antidote distribution and partial quarantine. We improve over existing antidote distribution schemes based on personalized PageRank in two ways. First, we replace the constraint on the topology of this subnetwork described by Chung et al. [Internet Math. 6, 237 (2009)] that a large fraction of the value of the personalized PageRank vector must be contained in the local cluster, with a partial quarantine scheme. Second, we derive a different lower bound on the amount of antidote. We show that, under our antidote distribution scheme, the probability of the infection spreading to the whole network is bounded, and the infection inside the subnetwork will disappear after a period that is proportional to the logarithm of the number of initially infected nodes. We demonstrate the effectiveness of our strategy with numerical simulations of epidemics on benchmark networks. We also test our strategy on two examples of epidemics in real-world networks. Our strategy is dependent only on the rate of infection, the rate of recovery, and the topology around the initially infected nodes, and is independent of the rest of the network.

中文翻译:

通过解毒剂分配和部分隔离在本地集群中控制流行病

网络传播现象的研究,特别是疾病的传播,引起了网络科学研究界的极大兴趣。在本文中,我们表明,通过解毒剂分发和部分隔离相结合,可以在一个小的子网络中有效地遏制和抑制流行病的爆发。我们以两种方式改进了基于个性化 PageRank 的现有解毒剂分配方案。首先,我们替换 Chung等人描述的对该子网拓扑的约束[互联网数学。 6, 237 (2009)] 个性化 PageRank 向量的大部分值必须包含在本地集群中,并采用部分隔离方案。其次,我们推导出解毒剂数量的不同下限。我们表明,在我们的解毒分配方案下,感染传播到整个网络的概率是有界的,并且子网内部的感染将在与初始感染节点数的对数成正比的一段时间后消失。我们通过基准网络上的流行病数值模拟证明了我们策略的有效性。我们还在现实世界网络中的两个流行病示例上测试了我们的策略。我们的策略仅取决于感染率、恢复率和最初受感染节点周围的拓扑结构,
更新日期:2021-09-17
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