Controlling nosocomial infection based on structure of hospital social networks

Taro Ueno, Naoki Masuda*

*この研究の対応する著者

研究成果: Article査読

44 被引用数 (Scopus)

抄録

Nosocomial infection (i.e. infection in healthcare facilities) raises a serious public health problem, as implied by the existence of pathogens characteristic to healthcare facilities such as methicillin-resistant Staphylococcus aureus and hospital-mediated outbreaks of influenza and severe acute respiratory syndrome. For general communities, epidemic modeling based on social networks is being recognized as a useful tool. However, disease propagation may occur in a healthcare facility in a manner different from that in a urban community setting due to different network architecture. We simulate stochastic susceptible-infected-recovered dynamics on social networks, which are based on observations in a hospital in Tokyo, to explore effective containment strategies against nosocomial infection. The observed social networks in the hospital have hierarchical and modular structure in which dense substructure such as departments, wards, and rooms, are globally but only loosely connected, and do not reveal extremely right-skewed distributions of the number of contacts per individual. We show that healthcare workers, particularly medical doctors, are main vectors (i.e. transmitters) 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 (frequency of mediating connection between pairs of individuals along the shortest paths) is superior to vaccinating ones with large connectedness to others or randomly chosen individuals, which was suggested by previous model studies.

本文言語English
ページ(範囲)655-666
ページ数12
ジャーナルJournal of Theoretical Biology
254
3
DOI
出版ステータスPublished - 2008 10 7
外部発表はい

ASJC Scopus subject areas

  • 統計学および確率
  • モデリングとシミュレーション
  • 生化学、遺伝学、分子生物学(全般)
  • 免疫学および微生物学(全般)
  • 農業および生物科学(全般)
  • 応用数学

フィンガープリント

「Controlling nosocomial infection based on structure of hospital social networks」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル