Interacting epidemics on overlay networks
Interacting epidemics on overlay networks
复制标题
DOI：
10.1103/physreve.81.036118
复制
发表时间：
2010-03-01
影响因子：
2.4
通讯作者：
Jansen, Vincent A. A.
中科院分区：
文献类型：
--
作者：
Funk, Sebastian;Jansen, Vincent A. A.
在连接一组给定节点的网络上传播的多个病原体之间的相互作用是一个持续的理论挑战。在这里，我们旨在通过研究任意联合度分布的覆盖网络上两个不同过程的键渗流来理解这种相互作用。我们发现，如果两个网络上的度是正相关的，那么第一个病原体的爆发对随后在连接同一组节点的第二个网络上传播的另一个病原体具有免疫力是最有效的。在这种情况下，两个网络的度分布越不均匀，保护越强。另一方面，如果这些程度是不相关的或负相关的，则增加的异质性降低了第一个过程防止第二个过程达到流行比例的可能性。我们将这些结果推广到两个网络的边重叠到任意量的情况，或者所给予的免疫力只是部分的情况。如果这两个进程相互授予豁免权，我们会发现一系列可能的共存或互斥情况，这取决于基础网络的联合程度分布和相互授予的豁免权的数量。这些结果推广了共存阈值的概念，说明了大规模网络结构对多个传播者之间相互作用的影响。
The interaction between multiple pathogens spreading on networks connecting a given set of nodes presents an ongoing theoretical challenge. Here, we aim to understand such interactions by studying bond percolation of two different processes on overlay networks of arbitrary joint degree distribution. We find that an outbreak of a first pathogen providing immunity to another one spreading subsequently on a second network connecting the same set of nodes does so most effectively if the degrees on the two networks are positively correlated. In that case, the protection is stronger the more heterogeneous the degree distributions of the two networks are. If, on the other hand, the degrees are uncorrelated or negatively correlated, increasing heterogeneity reduces the potential of the first process to prevent the second one from reaching epidemic proportions. We generalize these results to cases where the edges of the two networks overlap to arbitrary amount, or where the immunity granted is only partial. If both processes grant immunity to each other, we find a wide range of possible situations of coexistence or mutual exclusion, depending on the joint degree distribution of the underlying networks and the amount of immunity granted mutually. These results generalize the concept of a coexistence threshold and illustrate the impact of large-scale network structure on the interaction between multiple spreading agents.