About: Controlling nosocomial infection based on structure of hospital social networks

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

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Controlling nosocomial infection based on structure of hospital social networks
Creator	Masuda, Naoki Ueno, Taro
source	ArXiv
abstract	Nosocomial infection raises a serious public health problem, as implied by the existence of pathogens characteristic to healthcare and hospital-mediated outbreaks of influenza and SARS. We simulate stochastic SIR dynamics on social networks, which are based on observations in a hospital in Tokyo, to explore effective containment strategies against nosocomial infection. The observed networks have hierarchical and modular structure. We show that healthcare workers, particularly medical doctors, are main vectors of diseases on these networks. Intervention methods that restrict interaction between medical doctors and their visits to different wards shrink the final epidemic size more than intervention methods that directly protect patients, such as isolating patients in single rooms. By the same token, vaccinating doctors with priority rather than patients or nurses is more effective. Finally, vaccinating individuals with large betweenness centrality is superior to vaccinating ones with large connectedness to others or randomly chosen individuals, as suggested by previous model studies. [The abstract of the manuscript has more information.]
has issue date	2008-03-13(xsd:dateTime)
has license	arxiv
sha1sum (hex)	5d7dc504177db9326327e5cc7417bfa045e0f8ad
resource representing a document's title	Controlling nosocomial infection based on structure of hospital social networks
resource representing a document's body	covid:5d7dc504177db9326327e5cc7417bfa045e0f8ad#body_text
is schema:about of	named entity 'right-skewed' named entity 'Tokyo' named entity 'medical doctors' named entity 'public health' named entity 'stochastic' »more»

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