Epidemic processes in complex networks
Epidemic processes in complex networks
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DOI：
10.1103/revmodphys.87.925
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发表时间：
2015-08-31
影响因子：
44.1
通讯作者：
Vespignani, Alessandro
中科院分区：
文献类型：
--
作者：
Pastor-Satorras, Romualdo;Castellano, Claudio;Vespignani, Alessandro
近年来，研究界已经积累了大量证据，表明在广泛的生物和社会技术系统中出现了复杂且异构的连接模式。现实世界网络的复杂特性对各种系统中发生的平衡和非平衡现象的行为具有深远影响，而传染病传播的研究对于我们理解复杂网络中动态过程的展开至关重要。对异构网络中传染病传播的理论分析需要开发新的分析框架，并且已经产生了具有概念和实际相关性的结果。本文对有关传染病传播过程的大量研究活动进行了连贯而全面的综述，详细介绍了成功的理论方法，并明确了它们的局限性和假设。物理学家、数学家、流行病学家、计算机科学家和社会科学家在研究传染病传播方面有共同的兴趣，并依赖类似的模型来描述病原体、知识和创新的传播。出于这个原因，在关注传染病建模的主要结果和范例模型的同时，也介绍了有关广义社会传染过程的主要结果。最后，报告了在共同进化、耦合和时变网络中传染病传播研究的前沿研究活动。
In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.