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The Hidden Geometry of Complex, Network-Driven Contagion Phenomena


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        流行病，谣言，观点和创新的全球传播是复杂的，由网络驱动的动态过程。 底层网络的多尺度性质和固有异质性相结合，使人们难以对这些过程形成直观的了解，难以将其与外围因素区分开来，预测其时程并找到其起源。 但是，我们表明，如果用概率性有效距离代替常规地理距离，则复杂的时空模式可以简化为出奇的简单，均匀的波传播模式。 在全球空中交通媒介流行的背景下，我们表明有效距离可以可靠地预测疾病的到达时间。 即使流行病学参数未知，该方法仍可以提供相对到达时间。 该方法还可以确定传播过程的空间起源，并成功地应用于全球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.

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