The hidden geometry of complex, network-driven contagion phenomena.
Identifieur interne : 001236 ( Main/Exploration ); précédent : 001235; suivant : 001237The hidden geometry of complex, network-driven contagion phenomena.
Auteurs : Dirk Brockmann [Allemagne] ; Dirk HelbingSource :
- Science (New York, N.Y.) [ 1095-9203 ] ; 2013.
Descripteurs français
- KwdFr :
- Analyse spatio-temporelle, Densité de population, Flambées de maladies (), Grippe humaine (épidémiologie), Humains, Maladies transmissibles émergentes (épidémiologie), Migration humaine (), Modèles biologiques, Pandémies (), Pronostic, Simulation numérique, Sous-type H1N1 du virus de la grippe A, Syndrome respiratoire aigu sévère (épidémiologie), Virus du SRAS.
- MESH :
- épidémiologie : Grippe humaine, Maladies transmissibles émergentes, Syndrome respiratoire aigu sévère.
- Analyse spatio-temporelle, Densité de population, Flambées de maladies, Humains, Migration humaine, Modèles biologiques, Pandémies, Pronostic, Simulation numérique, Sous-type H1N1 du virus de la grippe A, Virus du SRAS.
English descriptors
- KwdEn :
- Communicable Diseases, Emerging (epidemiology), Computer Simulation, Disease Outbreaks (statistics & numerical data), Human Migration (statistics & numerical data), Humans, Influenza A Virus, H1N1 Subtype, Influenza, Human (epidemiology), Models, Biological, Pandemics (statistics & numerical data), Population Density, Prognosis, SARS Virus, Severe Acute Respiratory Syndrome (epidemiology), Spatio-Temporal Analysis.
- MESH :
- epidemiology : Communicable Diseases, Emerging, Influenza, Human, Severe Acute Respiratory Syndrome.
- statistics & numerical data : Disease Outbreaks, Human Migration, Pandemics.
- Computer Simulation, Humans, Influenza A Virus, H1N1 Subtype, Models, Biological, Population Density, Prognosis, SARS Virus, Spatio-Temporal Analysis.
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.
DOI: 10.1126/science.1245200
PubMed: 24337289
Affiliations:
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Le document en format XML
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SRAS</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Communicable Diseases, Emerging</term><term>Influenza, Human</term><term>Severe Acute Respiratory Syndrome</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Disease Outbreaks</term><term>Human Migration</term><term>Pandemics</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term><term>Maladies transmissibles émergentes</term><term>Syndrome respiratoire aigu sévère</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Computer Simulation</term><term>Humans</term><term>Influenza A Virus, H1N1 Subtype</term><term>Models, Biological</term><term>Population Density</term><term>Prognosis</term><term>SARS Virus</term><term>Spatio-Temporal Analysis</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Analyse spatio-temporelle</term><term>Densité de population</term><term>Flambées de maladies</term><term>Humains</term><term>Migration humaine</term><term>Modèles biologiques</term><term>Pandémies</term><term>Pronostic</term><term>Simulation numérique</term><term>Sous-type H1N1 du virus de la grippe A</term><term>Virus du SRAS</term></keywords></textClass></profileDesc></teiHeader><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></front></TEI><affiliations><list><country><li>Allemagne</li></country><region><li>Berlin</li></region><settlement><li>Berlin</li></settlement></list><tree><noCountry><name sortKey="Helbing, Dirk" sort="Helbing, Dirk" uniqKey="Helbing D" first="Dirk" last="Helbing">Dirk Helbing</name></noCountry><country name="Allemagne"><region name="Berlin"><name sortKey="Brockmann, Dirk" sort="Brockmann, Dirk" uniqKey="Brockmann D" first="Dirk" last="Brockmann">Dirk Brockmann</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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