1. Detecting the outbreak of influenza based on the shortest path of dynamic city network
- Author
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Rong Xie, Jialiu Xie, Yingqi Chen, Zhengrong Liu, Kun Yang, Pei Chen, and Rui Liu
- Subjects
Epidemiology ,Computer science ,Real-time computing ,lcsh:Medicine ,Influenza epidemics ,General Biochemistry, Genetics and Molecular Biology ,The shortest path ,03 medical and health sciences ,0302 clinical medicine ,Robustness (computer science) ,Dynamical network ,030304 developmental biology ,0303 health sciences ,Influenza outbreak ,General Neuroscience ,Pre-outbreak state ,lcsh:R ,Computational Biology ,Outbreak ,General Medicine ,Influenza pandemic ,030220 oncology & carcinogenesis ,Shortest path problem ,Dynamic city network ,Dynamic network marker ,Public Health ,General Agricultural and Biological Sciences - Abstract
The influenza pandemic causes a large number of hospitalizations and even deaths. There is an urgent need for an efficient and effective method for detecting the outbreak of influenza so that timely, appropriate interventions can be made to prevent or at least prepare for catastrophic epidemics. In this study, we proposed a computational method, the shortest-path-based dynamical network marker (SP-DNM), to detect the pre-outbreak state of influenza epidemics by monitoring the dynamical change of the shortest path in a city network. Specifically, by mapping the real-time information to a properly constructed city network, our method detects the early-warning signal prior to the influenza outbreak in both Tokyo and Hokkaido for consecutive 9 years, which demonstrate the effectiveness and robustness of the proposed method.
- Published
- 2020