The Hidden Geometry of Complex, Network-Driven Contagion Phenomena
The Hidden Geometry of Complex, Network-Driven Contagion Phenomena
复制标题
DOI：
10.1126/science.1245200
复制
发表时间：
2013-12-13
期刊：
影响因子：
56.9
通讯作者：
Helbing, Dirk
中科院分区：
文献类型：
--
作者：
Brockmann, Dirk;Helbing, Dirk
流行病、谣言、观点和创新的全球传播是复杂的、网络驱动的动态过程。底层网络的多尺度性质和内在异质性相结合，使得人们很难对这些过程产生直观的理解，难以区分相关因素和外围因素，难以预测其时间进程，并难以定位其起源。然而，我们表明，如果传统的地理距离被概率驱动的有效距离取代，复杂的时空模式可以简化为令人惊讶的简单、均匀的波传播模式。在全球空中交通介导的流行病的背景下，我们表明有效距离可以可靠地预测疾病到达时间。即使流行病学参数未知，该方法仍然可以提供相对到达时间。该方法还可以识别传播过程的空间起源，并成功应用于全球 2009 年 H1N1 流感大流行和 2003 年 SARS 流行的数据。
The global spread of epidemics, rumors, opinions, and innovations are complex, network-driven dynamic processes. The combined multiscale nature and intrinsic heterogeneity of the underlying networks make it difficult to develop an intuitive understanding of these processes, to distinguish relevant from peripheral factors, to predict their time course, and to locate their origin. However, we show that complex spatiotemporal patterns can be reduced to surprisingly simple, homogeneous wave propagation patterns, if conventional geographic distance is replaced by a probabilistically motivated effective distance. In the context of global, air-traffic-mediated epidemics, we show that effective distance reliably predicts disease arrival times. Even if epidemiological parameters are unknown, the method can still deliver relative arrival times. The approach can also identify the spatial origin of spreading processes and successfully be applied to data of the worldwide 2009 H1N1 influenza pandemic and 2003 SARS epidemic.