What's in a crowd? Analysis of face-to-face behavioral networks
What's in a crowd? Analysis of face-to-face behavioral networks
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DOI：
10.1016/j.jtbi.2010.11.033
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发表时间：
2011-02-21
影响因子：
2
通讯作者：
Van den Broeck, Wouter
中科院分区：
文献类型：
--
作者：
Isella, Lorenzo;Stehle, Juliette;Van den Broeck, Wouter
关于人员流动的新数据来源的出现为调查社交网络、人员流动和流行病传播等动态过程之间的相互作用开辟了新的途径。在这里，我们分析了大规模现实世界场景中个人的时间分辨面对面接近度的数据。我们比较了两个属性非常不同的设置，一个科学会议和一个长期运行的博物馆展览。我们跟踪面对面接近的行为网络，并从静态和动态的角度来描述它们，揭示差异和相似之处。我们使用我们的数据来研究在人类邻近的动态网络上展开的传染病传播的易感染模型的动力学。对于会议和博物馆的情况，传播模式是明显不同的，并且它们受到网络数据的因果结构的强烈影响。对传播路径的深入研究表明，仅仅知道静态聚集网络的信息会导致对动态网络上传播路径的错误结论。(C)2010爱思唯尔有限公司保留所有权利。
The availability of new data sources on human mobility is opening new avenues for investigating the interplay of social networks, human mobility and dynamical processes such as epidemic spreading. Here we analyze data on the time-resolved face-to-face proximity of individuals in large-scale real-world scenarios. We compare two settings with very different properties, a scientific conference and a long-running museum exhibition. We track the behavioral networks of face-to-face proximity, and characterize them from both a static and a dynamic point of view, exposing differences and similarities. We use our data to investigate the dynamics of a susceptible-infected model for epidemic spreading that unfolds on the dynamical networks of human proximity. The spreading patterns are markedly different for the conference and the museum case, and they are strongly impacted by the causal structure of the network data. A deeper study of the spreading paths shows that the mere knowledge of static aggregated networks would lead to erroneous conclusions about the transmission paths on the dynamical networks. (C) 2010 Elsevier Ltd. All rights reserved.