title: The hidden geometry of complex, network-driven contagion phenomena.
abstract: 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.
authors: Robert-Koch-Institute, Seestraße 10, 13353 Berlin, Germany.
authors: Dirk
authors: D
authors: Brockmann
authors: Dirk
authors: D
authors: Helbing
mesh_terms: D021821:Communicable Diseases, Emerging / Q000453:epidemiology*; D003198:Computer Simulation; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D063426:Human Migration / Q000706:statistics & numerical data*; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype; D007251:Influenza, Human / Q000453:epidemiology; D008954:Models, Biological; D058873:Pandemics / Q000706:statistics & numerical data; D011156:Population Density; D011379:Prognosis; D045473:Severe acute respiratory syndrome-related coronavirus; D045169:Severe Acute Respiratory Syndrome / Q000453:epidemiology; D062211:Spatio-Temporal Analysis