The Hidden Geometry of Complex, Network-Driven Contagion Phenomena
Predicting Disease Dissemination
In combating the global spread of an emerging infectious disease, answers must be obtained to three crucial questions: Where did the disease emerge? Where will it go next? When will it arrive? Brockmann and Helbing (p. 1337; see the Perspective by McLean) analyzed disease spread via the “effective distance” rather than geographical distance, wherein two locations that are connected by a strong link are effectively close. The approach was successfully applied to predict disease arrival times or disease source using data from the the 2003 SARS viral epidemic, 2009 H1N1 influenza pandemic, and the 2011 foodborne enterohaemorrhagic Escherichia coli outbreak in Germany.
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.
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Published In
Science
Volume 342 | Issue 6164
13 December 2013
13 December 2013
Copyright
Copyright © 2013, American Association for the Advancement of Science.
Submission history
Received: 27 August 2013
Accepted: 25 October 2013
Published in print: 13 December 2013
Acknowledgments
D.B. thanks W. Kath, D. Grady, W. H. Grond, O. Woolley-Meza, and A. Bentley for fruitful discussions and comments and W. Moers, B. May, and FuturICT for inspiration. We thank R. Brune for contributions during the early phase of the project and for work on the origin reconstruction and C. Thiemann for the development of the SPaTo network visualization tool (www.spato.net). Global mobility data was provided by OAG (www.oag.com), prevalence data of H1N1 and SARS by the WHO (www.who.int), and EHEC data by the RKI-Survstat (www3.rki.de/SurvStat/). This work was supported by the Volkswagen Foundation (project: “Bioinvasion and epidemic spread in complex transportation networks”) and partially supported by the ETH project “Systemic Risks, Systemic Solutions” (CHIRP II project ETH 48 12-1).
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