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Real-Time Forecast of Influenza Outbreak Using Dynamic Network Marker Based on Minimum Spanning Tree
Joint Authors
Yang, Kun
Xie, Jialiu
Xie, Rong
Pan, Yucong
Chen, Pei
Liu, Rui
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-01
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
The influenza pandemic is a wide-ranging threat to people’s health and property all over the world.
Developing effective strategies for predicting the influenza outbreak which may prevent or at least get ready for a new influenza pandemic is now a top global public health priority.
Owing to the complexity of influenza outbreaks that are usually involved with spatial and temporal characteristics of both biological and social systems, however, it is a challenging task to achieve the real-time monitoring of influenza outbreaks.
In this study, by exploring the rich dynamical information of the city network during influenza outbreaks, we developed a computational method, the minimum-spanning-tree-based dynamical network marker (MST-DNM), to identify the tipping point or critical stage prior to the influenza outbreak.
With historical records of influenza outpatients between 2009 and 2018, the MST-DNM strategy has been validated by accurate predictions of the influenza outbreaks in three Japanese cities/regions, respectively, i.e., Tokyo, Osaka, and Hokkaido.
These successful applications show that the early-warning signal was detected 4 weeks on average ahead of each influenza outbreak.
The results show that our method is of considerable potential in the practice of public health surveillance.
American Psychological Association (APA)
Yang, Kun& Xie, Jialiu& Xie, Rong& Pan, Yucong& Liu, Rui& Chen, Pei. 2020. Real-Time Forecast of Influenza Outbreak Using Dynamic Network Marker Based on Minimum Spanning Tree. BioMed Research International،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1136779
Modern Language Association (MLA)
Yang, Kun…[et al.]. Real-Time Forecast of Influenza Outbreak Using Dynamic Network Marker Based on Minimum Spanning Tree. BioMed Research International No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1136779
American Medical Association (AMA)
Yang, Kun& Xie, Jialiu& Xie, Rong& Pan, Yucong& Liu, Rui& Chen, Pei. Real-Time Forecast of Influenza Outbreak Using Dynamic Network Marker Based on Minimum Spanning Tree. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1136779
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1136779