Controlling nosocomial infection based on structure of hospital social networks

TL;DR

This study uses social network simulations based on a Tokyo hospital to identify effective strategies for controlling nosocomial infections, highlighting the importance of targeting healthcare workers, especially doctors, for vaccination and interaction restrictions.

Contribution

It demonstrates that intervention strategies focusing on healthcare workers, particularly doctors, are more effective than patient-focused measures in reducing hospital-acquired infections.

Findings

Restricting doctor interactions reduces epidemic size more than patient isolation.

Prioritizing vaccination for doctors is more effective than vaccinating patients or nurses.

Vaccinating individuals with high betweenness centrality outperforms other vaccination strategies.

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.]