Balcan Duygu, Vespignani Alessandro
Center for Complex Networks and Systems Research (CNetS), School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA.
Nat Phys. 2011 Jul 1;7:581-586. doi: 10.1038/nphys1944.
Human mobility and activity patterns mediate contagion on many levels, including: spatial spread of infectious diseases, diffusion of rumors, and emergence of consensus. These patterns however are often dominated by specific locations and recurrent flows and poorly modeled by the random diffusive dynamics generally used to study them. Here we develop a theoretical framework to analyze contagion within a network of locations where individuals recall their geographic origins. We find a phase transition between a regime in which the contagion affects a large fraction of the system and one in which only a small fraction is affected. This transition cannot be uncovered by continuous models due to the stochastic features of the contagion process and defines an invasion threshold that depends on mobility parameters, providing guidance for controlling contagion spread by constraining mobility processes. We recover the threshold behavior by analyzing diffusion processes mediated by real human commuting data.
人类的移动和活动模式在许多层面上介导了传播,包括:传染病的空间传播、谣言的扩散以及共识的形成。然而,这些模式通常由特定地点和反复出现的流动主导,并且一般用于研究它们的随机扩散动力学对其建模效果不佳。在此,我们开发了一个理论框架,用于分析个体能回忆起其地理来源的地点网络内的传播情况。我们发现,在一种传播影响系统很大一部分的状态与仅影响一小部分的状态之间存在相变。由于传播过程的随机特征,连续模型无法揭示这种转变,并且它定义了一个取决于移动参数的入侵阈值,为通过约束移动过程来控制传播扩散提供了指导。我们通过分析由真实人类通勤数据介导的扩散过程来恢复阈值行为。