Controlling nosocomial infection based on structure of hospital social networks

Ueno, Taro; Masuda, Naoki

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.]