scholarly journals Detecting the outbreak of influenza based on the shortest path of dynamic city network

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9432 ◽  
Author(s):  
Yingqi Chen ◽  
Kun Yang ◽  
Jialiu Xie ◽  
Rong Xie ◽  
Zhengrong Liu ◽  
...  
Keyword(s):  
Shortest Path ◽  
Early Warning ◽  
Influenza Pandemic ◽  
Warning Signal ◽  
Computational Method ◽  
Influenza Outbreak ◽  
City Network ◽  
Real Time Information ◽  
Time Information ◽  
Dynamical Change

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.

scholarly journals Real-Time Forecast of Influenza Outbreak Using Dynamic Network Marker Based on Minimum Spanning Tree

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Kun Yang ◽  
Jialiu Xie ◽  
Rong Xie ◽  
Yucong Pan ◽  
Rui Liu ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Influenza Pandemic ◽  
Social Systems ◽  
Computational Method ◽  
Global Public Health ◽  
Influenza Outbreak ◽  
Influenza Outbreaks

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.

Modern Navigation Systems and Related Spatial Query

2013 ◽  
pp. 631-637 ◽  
Author(s):  
Wei-Shinn Ku ◽  
Haojun Wang ◽  
Roger Zimmermann
Keyword(s):  
Shortest Path ◽  
Search Algorithms ◽  
Navigation Systems ◽  
Computational Power ◽  
Spatial Query ◽  
Positioning Systems ◽  
Path Search ◽  
Real Time Information ◽  
Shortest Path Search ◽  
Time Information

With the availability and accuracy of satellite-based positioning systems and the growing computational power of mobile devices, recent research and commercial products of navigation systems are focusing on incorporating real-time information for supporting various applications. In addition, for routing purposes, navigation systems implement many algorithms related to path finding (e.g., shortest path search algorithms). This chapter presents the foundation and state-of-the-art development of navigation systems and reviews several spatial query related algorithms.

scholarly journals Real-time forecast of influenza outbreak using dynamic network marker based on minimum spanning tree

2020 ◽  
Author(s):  
Kun Yang ◽  
Jialiu Xie ◽  
Rong Xie ◽  
Yucong Pan ◽  
Rui Liu ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Influenza Pandemic ◽  
Social Systems ◽  
Computational Method ◽  
Global Public Health ◽  
Influenza Outbreak ◽  
Influenza Outbreaks

Abstract Background: 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.Methods: 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.Results: 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, Hokkaido. These successful applications show that the early-warning signal was detected 4 weeks on average ahead of each influenza outbreak.Conclusion: The results show that our method is of considerable potential in the practice of public health surveillance.

scholarly journals Forecasting the spatial transmission of influenza in the United States

2018 ◽  
Vol 115 (11) ◽  
pp. 2752-2757 ◽  
Author(s):  
Sen Pei ◽  
Sasikiran Kandula ◽  
Wan Yang ◽  
Jeffrey Shaman
Keyword(s):  
Human Mobility ◽  
State Level ◽  
Human Movement ◽  
The United States ◽  
Spatial Spread ◽  
Influenza Outbreak ◽  
Mobility Data ◽  
Real Time Information ◽  
Time Information ◽  
Spatial Transmission

Recurrent outbreaks of seasonal and pandemic influenza create a need for forecasts of the geographic spread of this pathogen. Although it is well established that the spatial progression of infection is largely attributable to human mobility, difficulty obtaining real-time information on human movement has limited its incorporation into existing infectious disease forecasting techniques. In this study, we develop and validate an ensemble forecast system for predicting the spatiotemporal spread of influenza that uses readily accessible human mobility data and a metapopulation model. In retrospective state-level forecasts for 35 US states, the system accurately predicts local influenza outbreak onset,—i.e., spatial spread, defined as the week that local incidence increases above a baseline threshold—up to 6 wk in advance of this event. In addition, the metapopulation prediction system forecasts influenza outbreak onset, peak timing, and peak intensity more accurately than isolated location-specific forecasts. The proposed framework could be applied to emergent respiratory viruses and, with appropriate modifications, other infectious diseases.

Modern Navigation Systems and Related Spatial Query

2010 ◽  
pp. 285-292
Author(s):  
Wei-Shinn Ku ◽  
Haojun Wang ◽  
Roger Zimmermann
Keyword(s):  
Shortest Path ◽  
Search Algorithms ◽  
Navigation Systems ◽  
Computational Power ◽  
Spatial Query ◽  
Positioning Systems ◽  
Path Search ◽  
Real Time Information ◽  
Shortest Path Search ◽  
Time Information

With the availability and accuracy of satellite-based positioning systems and the growing computational power of mobile devices, recent research and commercial products of navigation systems are focusing on incorporating real-time information for supporting various applications. In addition, for routing purposes, navigation systems implement many algorithms related to path finding (e.g., shortest path search algorithms). This chapter presents the foundation and state-of-the-art development of navigation systems and reviews several spatial query related algorithms.

Early Warning System Based on GIS and WSN in Tea Garden

2013 ◽  
Vol 427-429 ◽  
pp. 2155-2158
Author(s):  
Li Zhou ◽  
Shu Hui Zhang ◽  
Yan Dong Song
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Temperature Inversion ◽  
Work Status ◽  
Tea Garden ◽  
Real Time Information ◽  
Time Information

The rational database including the distribution, changes and testing facilities status of temperature & humidity in tea garden is created using the MapInfo 9.5. A set of the tea garden wireless utilities is established using the ZigBee module. The temperature & humidity real-time monitoring system and early warning system for Frost Prevention is established using Mapbasic. This system can get real-time information of temperature & humidity of nodes in different locations and give frost warning early. The frost prevention fans can be timely started, once temperature inversion happens. At the same time, the manager of tea garden can realize real-time monitor of the work status and quick location of facilities with the help of GPS.

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