Controlling nosocomial infection based on structure of hospital social networks

Biology – Quantitative Biology – Populations and Evolution

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

12 figures, 2 tables

Scientific paper

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.]

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Controlling nosocomial infection based on structure of hospital social networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Controlling nosocomial infection based on structure of hospital social networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Controlling nosocomial infection based on structure of hospital social networks will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-69442

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.