About: Insights into the transmission of respiratory infectious diseases through empirical human contact networks

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Insights into the transmission of respiratory infectious diseases through empirical human contact networks
Creator	Wang, Chao He, Guangxue Zhou, Yang Zhao, Yanlin Bu, Dongbo Sun, Shiwei Deng, Minghua Huang, Chunlin Liu, Xingwu Zhang, Haicang Cheng, Shuai
Source	PMC
abstract	In this study, we present representative human contact networks among Chinese college students. Unlike schools in the US, human contacts within Chinese colleges are extremely clustered, partly due to the highly organized lifestyle of Chinese college students. Simulations of influenza spreading across real contact networks are in good accordance with real influenza records; however, epidemic simulations across idealized scale-free or small-world networks show considerable overestimation of disease prevalence, thus challenging the widely-applied idealized human contact models in epidemiology. Furthermore, the special contact pattern within Chinese colleges results in disease spreading patterns distinct from those of the US schools. Remarkably, class cancelation, though simple, shows a mitigating power equal to quarantine/vaccination applied on ~25% of college students, which quantitatively explains its success in Chinese colleges during the SARS period. Our findings greatly facilitate reliable prediction of epidemic prevalence, and thus should help establishing effective strategies for respiratory infectious diseases control.
has issue date	2016-08-16(xsd:dateTime)
bibo:doi	10.1038/srep31484
bibo:pmid	27526868
has license	cc-by
sha1sum (hex)	25c68946a75609c08c00a639ba839a67316cd034
schema:url	https://doi.org/10.1038/srep31484
resource representing a document's title	Insights into the transmission of respiratory infectious diseases through empirical human contact networks
has PubMed Central identifier	PMC4985757
has PubMed identifier	27526868
schema:publication	Sci Rep
resource representing a document's body	covid:25c68946a75609c08c00a639ba839a67316cd034#body_text
is schema:about of	named entity 'RESPIRATORY' named entity 'EMPIRICAL' named entity 'REGULAR' named entity 'Methods' named entity 'human' named entity 'TRANSMISSION' named entity 'DISTINGUISHING' named entity 'NETWORKS' named entity 'TRANSIENT' named entity 'INSIGHTS' named entity 'FORMED' named entity 'METHODS' named entity 'INFECTIOUS DISEASES' named entity 'CONTACT' named entity 'INFORMATION' named entity '1.1' named entity 'INTERACTIONS' named entity 'RANDOM' named entity 'HUMAN' named entity 'CPIS' named entity '1.1' named entity 'clustering coefficient' named entity 'Poisson distribution' named entity 'eigenvector' named entity 'shortest paths' named entity 'November 3' named entity 'nonincreasing' named entity 'sampling' named entity 'probability' named entity 'histogram' named entity 'probability' named entity 'Bluetooth' named entity 'sample size' named entity 'log-log scale' named entity 'subgraphs' named entity 'sample size' named entity 'geometric distribution' named entity 'eigenvector centrality' named entity 'geometric distribution' named entity 'shortest paths' named entity 'network analysis' named entity 'probability' named entity 'small-world network' named entity 'statistical model' named entity 'Algorithm' named entity 'probability' named entity 'control strategy' named entity 'Eigenvector centrality' named entity 'vertices' named entity 'geometric distribution' named entity 'probability' named entity 'Eigenvector centrality' named entity 'sampling' named entity 'Algorithm' named entity 'subset' named entity 'integer' named entity 'sample size' named entity 'randomly chosen' named entity '1, 2' named entity '0.01' named entity 'infectious diseases' named entity 'sample size' named entity 'node degree' named entity 'variance' named entity 'Algorithm' named entity 'network-based' named entity 'betweenness centrality' named entity 'Poisson' named entity 'betweenness centrality' named entity 'eigenvector' named entity 'large-scale data collection'

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