Epidemic modeling in complex realities.
Identifieur interne : 003716 ( Main/Exploration ); précédent : 003715; suivant : 003717Epidemic modeling in complex realities.
Auteurs : Vittoria Colizza [États-Unis] ; Marc Barthélemy ; Alain Barrat ; Alessandro VespignaniSource :
- Comptes rendus biologies [ 1631-0691 ] ; 2007.
Descripteurs français
- KwdFr :
- MESH :
- mortalité : Peste.
- épidémiologie : Europe, Peste, Syndrome respiratoire aigu sévère.
- Facteurs socioéconomiques, Humains, Population, Santé mondiale, Virus du SRAS, Voyage, Épidémiologie.
English descriptors
- KwdEn :
- MESH :
- geographic , epidemiology : Europe.
- epidemiology : Plague, Severe Acute Respiratory Syndrome.
- mortality : Plague.
- statistics & numerical data : Epidemiology.
- Global Health, Humans, Population, SARS Virus, Socioeconomic Factors, Travel.
Abstract
In our global world, the increasing complexity of social relations and transport infrastructures are key factors in the spread of epidemics. In recent years, the increasing availability of computer power has enabled both to obtain reliable data allowing one to quantify the complexity of the networks on which epidemics may propagate and to envision computational tools able to tackle the analysis of such propagation phenomena. These advances have put in evidence the limits of homogeneous assumptions and simple spatial diffusion approaches, and stimulated the inclusion of complex features and heterogeneities relevant in the description of epidemic diffusion. In this paper, we review recent progresses that integrate complex systems and networks analysis with epidemic modelling and focus on the impact of the various complex features of real systems on the dynamics of epidemic spreading.
DOI: 10.1016/j.crvi.2007.02.014
PubMed: 17502293
Affiliations:
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Le document en format XML
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<term>Plague (epidemiology)</term>
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<term>Peste (épidémiologie)</term>
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<front><div type="abstract" xml:lang="en">In our global world, the increasing complexity of social relations and transport infrastructures are key factors in the spread of epidemics. In recent years, the increasing availability of computer power has enabled both to obtain reliable data allowing one to quantify the complexity of the networks on which epidemics may propagate and to envision computational tools able to tackle the analysis of such propagation phenomena. These advances have put in evidence the limits of homogeneous assumptions and simple spatial diffusion approaches, and stimulated the inclusion of complex features and heterogeneities relevant in the description of epidemic diffusion. In this paper, we review recent progresses that integrate complex systems and networks analysis with epidemic modelling and focus on the impact of the various complex features of real systems on the dynamics of epidemic spreading.</div>
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