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The Hidden Geometry of Complex, Network-Driven Contagion Phenomena

Brockmann, Dirk ; Helbing, Dirk

Science (American Association for the Advancement of Science), 2013-12, Vol.342 (6164), p.1337-1342 [Periódico revisado por pares]

Washington, DC: American Association for the Advancement of Science

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Título:
The Hidden Geometry of Complex, Network-Driven Contagion Phenomena
Autor: Brockmann, Dirk ; Helbing, Dirk
Assuntos: Biological and medical sciences ; Communicable Diseases ; Communicable Diseases, Emerging - epidemiology ; Computer Simulation ; Computerized, statistical medical data processing and models in biomedicine ; Disease models ; Disease outbreaks ; Disease Outbreaks - statistics & numerical data ; Disease transmission ; Emerging diseases ; Epidemics ; Epidemiology ; Escherichia coli ; General aspects ; Geometry ; H1N1 subtype influenza A virus ; Human infectious diseases. Experimental studies and models ; Human Migration - statistics & numerical data ; Human viral diseases ; Humans ; Infectious diseases ; Influenza ; Influenza A Virus, H1N1 Subtype ; Influenza, Human - epidemiology ; Links ; Medical sciences ; Modeling ; Models and simulation ; Models, Biological ; Outbreaks ; Pandemics ; Pandemics - statistics & numerical data ; Population Density ; Prognosis ; RESEARCH ARTICLE ; SARS ; SARS Virus ; Severe Acute Respiratory Syndrome - epidemiology ; Simulations ; Spatio-Temporal Analysis ; Spreads ; Viral diseases ; Viral diseases of the respiratory system and ent viral diseases
É parte de: Science (American Association for the Advancement of Science), 2013-12, Vol.342 (6164), p.1337-1342
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
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Descrição: 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.
Editor: Washington, DC: American Association for the Advancement of Science
Idioma: Inglês

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The Hidden Geometry of Complex, Network-Driven Contagion Phenomena

Brockmann, Dirk ; Helbing, Dirk

Washington, DC: American Association for the Advancement of Science

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