About: Traffic-driven SIR epidemic spreading in networks

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Traffic-driven SIR epidemic spreading in networks
Creator	Ji, Zexuan Li, Siyuan Pu, Cunlai Xu, Zhongqi Yang, Jian Yang, Xianxia
Source	ArXiv; Elsevier; Medline; PMC
abstract	Abstract We study SIR epidemic spreading in networks driven by traffic dynamics, which are further governed by static routing protocols. We obtain the maximum instantaneous population of infected nodes and the maximum population of ever infected nodes through simulation. We find that generally more balanced load distribution leads to more intense and wide spread of an epidemic in networks. Increasing either average node degree or homogeneity of degree distribution will facilitate epidemic spreading. When packet generation rate ρ is small, increasing ρ favors epidemic spreading. However, when ρ is large enough, traffic congestion appears which inhibits epidemic spreading.
has issue date	2016-03-15(xsd:dateTime)
bibo:doi	10.1016/j.physa.2015.11.028
bibo:pmid	32288096
has license	els-covid
sha1sum (hex)	5be5d9505613508634f87e49357649c9cad7981f
schema:url	https://doi.org/10.1016/j.physa.2015.11.028
resource representing a document's title	Traffic-driven SIR epidemic spreading in networks
has PubMed Central identifier	PMC7127125
has PubMed identifier	32288096
schema:publication	Physica A: Statistical Mechanics and its Applications
resource representing a document's body	covid:5be5d9505613508634f87e49357649c9cad7981f#body_text
is schema:about of	named entity 'DRIVEN' named entity 'SPREADING' named entity 'H L' named entity 'T S' covid:arg/5be5d9505613508634f87e49357649c9cad7981f named entity 'G H' named entity 'EPIDEMIC' named entity 'TRAFFIC' named entity 'SIR' named entity 'NETWORKS' named entity 'epidemic' named entity 'power-law' named entity 'scale-free networks' named entity 'epidemic' named entity 'routing protocol' named entity 'Swine flu' named entity 'infection' named entity 'infection' named entity 'infection' named entity 'thermodynamic limit' named entity 'epidemic' named entity 'infection' named entity 'cost function' named entity 'H1N1' named entity 'epidemic' named entity 'ER model' named entity 'probability' named entity 'immunization' named entity 'traffic congestion' named entity 'Epidemic' named entity 'epidemic' named entity 'epidemic' named entity 'Ebola virus' named entity 'infection' named entity 'epidemic' named entity 'mathematics' named entity 'mobile phone' named entity 'infection' named entity 'epidemic' named entity 'network flow' named entity 'message-passing' named entity 'traffic congestion' named entity 'epidemic' named entity 'epidemic' named entity 'scale-free networks' named entity 'epidemic' named entity 'traffic congestion' named entity 'Flu' named entity 'traffic congestion' named entity 'traffic congestion' named entity 'traffic congestion' named entity 'epidemic' named entity 'epidemic' named entity 'epidemic' named entity 'viruses' named entity 'epidemic model' named entity 'epidemic' named entity 'static routing' named entity 'FIFO' named entity 'routing protocol' named entity 'epidemics' named entity 'epidemic' named entity 'epidemics' named entity 'percolation' named entity 'traffic congestion' named entity 'epidemic' named entity 'scale-free networks' named entity 'traffic congestion' named entity 'traffic congestion' named entity 'epidemic model' named entity 'epidemic' named entity 'HIV' named entity 'epidemics' named entity 'flow state' named entity 'traffic congestion' named entity 'randomly select'

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