Clinical Research Papers:

Use of social network analysis and global sensitivity and uncertainty analyses to better understand an influenza outbreak

Jianhua Liu, Hongbo Jiang, Hao Zhang, Chun Guo, Lei Wang, Jing Yang and Shaofa Nie _

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Oncotarget. 2017; 8:43417-43426. https://doi.org/10.18632/oncotarget.15076

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Jianhua Liu1,3, Hongbo Jiang2, Hao Zhang3, Chun Guo1, Lei Wang1,3, Jing Yang3 and Shaofa Nie1

1 Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China

2 Department of Epidemiology and Biostatistics, School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China

3 Department of Infectious Diseases, Center for Disease Control and Prevention, Yichang City, Hubei, China

Correspondence to:

Shaofa Nie, email:

Keywords: field epidemiology, control of infectious diseases, social network analysis, global sensitivity and uncertainty analyses

Received: November 02, 2016 Accepted: January 11, 2017 Published: February 03, 2017


In the summer of 2014, an influenza A(H3N2) outbreak occurred in Yichang city, Hubei province, China. A retrospective study was conducted to collect and interpret hospital and epidemiological data on it using social network analysis and global sensitivity and uncertainty analyses. Results for degree (χ2=17.6619, P<0.0001) and betweenness(χ2=21.4186, P<0.0001) centrality suggested that the selection of sampling objects were different between traditional epidemiological methods and newer statistical approaches. Clique and network diagrams demonstrated that the outbreak actually consisted of two independent transmission networks. Sensitivity analysis showed that the contact coefficient (k) was the most important factor in the dynamic model. Using uncertainty analysis, we were able to better understand the properties and variations over space and time on the outbreak. We concluded that use of newer approaches were significantly more efficient for managing and controlling infectious diseases outbreaks, as well as saving time and public health resources, and could be widely applied on similar local outbreaks.

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