PandemieGrippaleV1, PascalFrancis, Corpus, bibRecord, 000033

The Hidden Geometry of Complex, Network-Driven Contagion Phenomena

Identifieur interne : 000033 ( PascalFrancis/Corpus ); précédent : 000032; suivant : 000034

The Hidden Geometry of Complex, Network-Driven Contagion Phenomena

Auteurs : Dirk Brockmann ; Dirk Helbing

Source :

Science : (Washington, D.C.) [ 0036-8075 ] ; 2013.

RBID : Pascal:14-0241810

Descripteurs français

Pascal (Inist)
- Grippe, Syndrome respiratoire aigu sévère, Maladie émergente, Epidémie, Contagion, Modélisation, Homme, Statistique, Maladie contagieuse, Pandémie.

English descriptors

KwdEn :
- Communicable disease, Contagion, Emerging disease, Epidemic, Human, Influenza, Modeling, Severe acute respiratory syndrome, Statistics.

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.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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A02	`01`			`@0 SCIEAS`
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A08	`01`	`1`	`ENG`	`@1 The Hidden Geometry of Complex, Network-Driven Contagion Phenomena`
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A11	`02`	`1`		`@1 HELBING (Dirk)`
A14	`01`			`@1 Robert-Koch-Institute, Seestrasse 10 @2 13353 Berlin @3 DEU @Z 1 aut.`
A14	`02`			`@1 Institute for Theoretical Biology, Humboldt-University Berlin, Invalidenstrasse 42 @2 10115 Berlin @3 DEU @Z 1 aut.`
A14	`03`			`@1 Department of Engineering Sciences and Applied Mathematics and Northwestern Institute on Complex Systems, Northwestern University @2 Evanston, IL 60208 @3 USA @Z 1 aut.`
A14	`04`			`@1 ETH Zurich, Swiss Federal Institute of Technology, CLU E1, Clausiusstrasse 50 @2 8092 Zurich @3 CHE @Z 2 aut.`
A14	`05`			`@1 Risk Center, ETH Zurich, Scheuchzerstrasse 7 @2 8092 Zurich @3 CHE @Z 2 aut.`
A20				`@1 1337-1342`
A21				`@1 2013`
A23	`01`			`@0 ENG`
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A44				`@0 0000 @1 © 2014 INIST-CNRS. All rights reserved.`
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A61				`@0 A`
A64	`01`	`1`		`@0 Science : (Washington, D.C.)`
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C01	`01`		`ENG`	@0 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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C03	`01`	`X`	`ENG`	`@0 Influenza @5 01`
C03	`01`	`X`	`SPA`	`@0 Gripe @5 01`
C03	`02`	`X`	`FRE`	`@0 Syndrome respiratoire aigu sévère @2 NM @5 02`
C03	`02`	`X`	`ENG`	`@0 Severe acute respiratory syndrome @2 NM @5 02`
C03	`02`	`X`	`SPA`	`@0 Síndrome respiratorio agudo severo @2 NM @5 02`
C03	`03`	`X`	`FRE`	`@0 Maladie émergente @2 NM @5 03`
C03	`03`	`X`	`ENG`	`@0 Emerging disease @2 NM @5 03`
C03	`03`	`X`	`SPA`	`@0 Enfermedad emergente @2 NM @5 03`
C03	`04`	`X`	`FRE`	`@0 Epidémie @5 07`
C03	`04`	`X`	`ENG`	`@0 Epidemic @5 07`
C03	`04`	`X`	`SPA`	`@0 Epidemia @5 07`
C03	`05`	`X`	`FRE`	`@0 Contagion @5 08`
C03	`05`	`X`	`ENG`	`@0 Contagion @5 08`
C03	`05`	`X`	`SPA`	`@0 Contagio @5 08`
C03	`06`	`X`	`FRE`	`@0 Modélisation @5 09`
C03	`06`	`X`	`ENG`	`@0 Modeling @5 09`
C03	`06`	`X`	`SPA`	`@0 Modelización @5 09`
C03	`07`	`X`	`FRE`	`@0 Homme @5 13`
C03	`07`	`X`	`ENG`	`@0 Human @5 13`
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C03	`08`	`X`	`FRE`	`@0 Statistique @5 14`
C03	`08`	`X`	`ENG`	`@0 Statistics @5 14`
C03	`08`	`X`	`SPA`	`@0 Estadística @5 14`
C03	`09`	`X`	`FRE`	`@0 Maladie contagieuse @5 15`
C03	`09`	`X`	`ENG`	`@0 Communicable disease @5 15`
C03	`09`	`X`	`SPA`	`@0 Enfermedad contagiosa @5 15`
C03	`10`	`X`	`FRE`	`@0 Pandémie @4 INC @5 86`
C07	`01`	`X`	`FRE`	`@0 Virose`
C07	`01`	`X`	`ENG`	`@0 Viral disease`
C07	`01`	`X`	`SPA`	`@0 Virosis`
C07	`02`	`X`	`FRE`	`@0 Infection`
C07	`02`	`X`	`ENG`	`@0 Infection`
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C07	`03`	`X`	`FRE`	`@0 Pathologie de l'appareil respiratoire @5 37`
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C07	`03`	`X`	`SPA`	`@0 Aparato respiratorio patología @5 37`
C07	`04`	`X`	`FRE`	`@0 Pathologie des poumons @5 38`
C07	`04`	`X`	`ENG`	`@0 Lung disease @5 38`
C07	`04`	`X`	`SPA`	`@0 Pulmón patología @5 38`
N21				`@1 293`

Format Inist (serveur)

NO :	PASCAL 14-0241810 INIST
ET :	The Hidden Geometry of Complex, Network-Driven Contagion Phenomena
AU :	BROCKMANN (Dirk); HELBING (Dirk)
AF :	Robert-Koch-Institute, Seestrasse 10/13353 Berlin/Allemagne (1 aut.); Institute for Theoretical Biology, Humboldt-University Berlin, Invalidenstrasse 42/10115 Berlin/Allemagne (1 aut.); Department of Engineering Sciences and Applied Mathematics and Northwestern Institute on Complex Systems, Northwestern University/Evanston, IL 60208/Etats-Unis (1 aut.); ETH Zurich, Swiss Federal Institute of Technology, CLU E1, Clausiusstrasse 50/8092 Zurich/Suisse (2 aut.); Risk Center, ETH Zurich, Scheuchzerstrasse 7/8092 Zurich/Suisse (2 aut.)
DT :	Publication en série; Niveau analytique
SO :	Science : (Washington, D.C.); ISSN 0036-8075; Coden SCIEAS; Etats-Unis; Da. 2013; Vol. 342; No. 6164; Pp. 1337-1342; Bibl. 39 ref.
LA :	Anglais
EA :	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.
CC :	002B28B; 002B05A03; 002B05C02C
FD :	Grippe; Syndrome respiratoire aigu sévère; Maladie émergente; Epidémie; Contagion; Modélisation; Homme; Statistique; Maladie contagieuse; Pandémie
FG :	Virose; Infection; Pathologie de l'appareil respiratoire; Pathologie des poumons
ED :	Influenza; Severe acute respiratory syndrome; Emerging disease; Epidemic; Contagion; Modeling; Human; Statistics; Communicable disease
EG :	Viral disease; Infection; Respiratory disease; Lung disease
SD :	Gripe; Síndrome respiratorio agudo severo; Enfermedad emergente; Epidemia; Contagio; Modelización; Hombre; Estadística; Enfermedad contagiosa
LO :	INIST-6040.354000500700450200
ID :	14-0241810

Links to Exploration step

Pascal:14-0241810

Le document en format XML

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<front><div type="abstract" xml:lang="en">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.</div>
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<server><NO>PASCAL 14-0241810 INIST</NO>
<ET>The Hidden Geometry of Complex, Network-Driven Contagion Phenomena</ET>
<AU>BROCKMANN (Dirk); HELBING (Dirk)</AU>
<AF>Robert-Koch-Institute, Seestrasse 10/13353 Berlin/Allemagne (1 aut.); Institute for Theoretical Biology, Humboldt-University Berlin, Invalidenstrasse 42/10115 Berlin/Allemagne (1 aut.); Department of Engineering Sciences and Applied Mathematics and Northwestern Institute on Complex Systems, Northwestern University/Evanston, IL 60208/Etats-Unis (1 aut.); ETH Zurich, Swiss Federal Institute of Technology, CLU E1, Clausiusstrasse 50/8092 Zurich/Suisse (2 aut.); Risk Center, ETH Zurich, Scheuchzerstrasse 7/8092 Zurich/Suisse (2 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>Science : (Washington, D.C.); ISSN 0036-8075; Coden SCIEAS; Etats-Unis; Da. 2013; Vol. 342; No. 6164; Pp. 1337-1342; Bibl. 39 ref.</SO>
<LA>Anglais</LA>
<EA>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.</EA>
<CC>002B28B; 002B05A03; 002B05C02C</CC>
<FD>Grippe; Syndrome respiratoire aigu sévère; Maladie émergente; Epidémie; Contagion; Modélisation; Homme; Statistique; Maladie contagieuse; Pandémie</FD>
<FG>Virose; Infection; Pathologie de l'appareil respiratoire; Pathologie des poumons</FG>
<ED>Influenza; Severe acute respiratory syndrome; Emerging disease; Epidemic; Contagion; Modeling; Human; Statistics; Communicable disease</ED>
<EG>Viral disease; Infection; Respiratory disease; Lung disease</EG>
<SD>Gripe; Síndrome respiratorio agudo severo; Enfermedad emergente; Epidemia; Contagio; Modelización; Hombre; Estadística; Enfermedad contagiosa</SD>
<LO>INIST-6040.354000500700450200</LO>
<ID>14-0241810</ID>
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The Hidden Geometry of Complex, Network-Driven Contagion Phenomena

The Hidden Geometry of Complex, Network-Driven Contagion Phenomena

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