tetano
Editor, Senior Moderator
Biomed Res Int
. 2020 Oct 1;2020:7351398.
doi: 10.1155/2020/7351398. eCollection 2020.
Real-Time Forecast of Influenza Outbreak Using Dynamic Network Marker Based on Minimum Spanning Tree
Kun Yang[SUP] 1 [/SUP], Jialiu Xie[SUP] 2 [/SUP], Rong Xie[SUP] 3 [/SUP], Yucong Pan[SUP] 4 [/SUP], Rui Liu[SUP] 5 [/SUP], Pei Chen[SUP] 5 [/SUP]
Affiliations
Abstract
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.
. 2020 Oct 1;2020:7351398.
doi: 10.1155/2020/7351398. eCollection 2020.
Real-Time Forecast of Influenza Outbreak Using Dynamic Network Marker Based on Minimum Spanning Tree
Kun Yang[SUP] 1 [/SUP], Jialiu Xie[SUP] 2 [/SUP], Rong Xie[SUP] 3 [/SUP], Yucong Pan[SUP] 4 [/SUP], Rui Liu[SUP] 5 [/SUP], Pei Chen[SUP] 5 [/SUP]
Affiliations
- PMID: 33062696
- PMCID: PMC7547339
- DOI: 10.1155/2020/7351398
Abstract
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.