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Science. The Hidden Geometry of Complex, Network-Driven Contagion Phenomena
[Source: Science, full page: (LINK). Abstract, edited.]
<CITE><ABBR>Science</ABBR> 13 December 2013: Vol. 342 no. 6164 pp. 1337-1342 / DOI: 10.1126/science.1245200 </CITE>
<CITE></CITE>
<CITE></CITE>Research Article
The Hidden Geometry of Complex, Network-Driven Contagion Phenomena
Dirk Brockmann<SUP>1</SUP>,<SUP>2</SUP>,<SUP>3</SUP>,*, Dirk Helbing<SUP>4</SUP>,<SUP>5</SUP>
<SUP></SUP>
Author Affiliations: <SUP>1</SUP>Robert-Koch-Institute, Seestra?e 10, 13353 Berlin, Germany. <SUP>2</SUP>Institute for Theoretical Biology, Humboldt-University Berlin, Invalidenstra?e 42, 10115 Berlin, Germany. <SUP>3</SUP>Department of Engineering Sciences and Applied Mathematics and Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208, USA. <SUP>4</SUP>ETH Zurich, Swiss Federal Institute of Technology, CLU E1, Clausiusstra?e 50, 8092 Zurich, Switzerland. <SUP>5</SUP>Risk Center, ETH Zurich, Scheuchzerstra?e 7, 8092 Zurich, Switzerland.
*Corresponding author. E-mail: dirk.brockmann@hu-berlin.de
Abstract
Editor's Summary
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.
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.
Received for publication 27 August 2013. Accepted for publication 25 October 2013.
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<CITE><ABBR>Science</ABBR> 13 December 2013: Vol. 342 no. 6164 pp. 1337-1342 / DOI: 10.1126/science.1245200 </CITE>
<CITE></CITE>
<CITE></CITE>Research Article
The Hidden Geometry of Complex, Network-Driven Contagion Phenomena
Dirk Brockmann<SUP>1</SUP>,<SUP>2</SUP>,<SUP>3</SUP>,*, Dirk Helbing<SUP>4</SUP>,<SUP>5</SUP>
<SUP></SUP>
Author Affiliations: <SUP>1</SUP>Robert-Koch-Institute, Seestra?e 10, 13353 Berlin, Germany. <SUP>2</SUP>Institute for Theoretical Biology, Humboldt-University Berlin, Invalidenstra?e 42, 10115 Berlin, Germany. <SUP>3</SUP>Department of Engineering Sciences and Applied Mathematics and Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208, USA. <SUP>4</SUP>ETH Zurich, Swiss Federal Institute of Technology, CLU E1, Clausiusstra?e 50, 8092 Zurich, Switzerland. <SUP>5</SUP>Risk Center, ETH Zurich, Scheuchzerstra?e 7, 8092 Zurich, Switzerland.
*Corresponding author. E-mail: dirk.brockmann@hu-berlin.de
Abstract
Editor's Summary
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.
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.
Received for publication 27 August 2013. Accepted for publication 25 October 2013.
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