Detection of High-risk Zones and Potential Infected Neighbors from Infectious Disease Monitoring Data

TAN Biying; DUAN Lei; GOU Chi; HUANG Shuyang; FANG Yuhao; ZHAO Xing; TANG Changjie

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Detection of High-risk Zones and Potential Infected Neighbors from Infectious Disease Monitoring Data

TAN Biying 1,DUAN Lei 1 * #,GOU Chi 1,HUANG Shuyang 1,FANG Yuhao 1,ZHAO Xing 2,TANG Changjie 1

1.School of Computer Science, Sichuan University, Chengdu 610065

2.West China School of Public Health, Sichuan University, Chengdu 610041

*Correspondence author

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Funding: the Young Faculty Foundation of Sichuan University （No.No. 2009SCU11030）, National Natural Science Foundation of China （No.No. 61103042）, Specialized Research Fund for the Doctoral Program of Higher Education （No.No.20100181120029）

Opened online:30 December 2011

Accepted by: none

Citation: TAN Biying,DUAN Lei,GOU Chi.Detection of High-risk Zones and Potential Infected Neighbors from Infectious Disease Monitoring Data[OL]. [30 December 2011] http://en.paper.edu.cn/en_releasepaper/content/4457230

Detecting the high-risk zones as well as potential infected geographical neighbor is necessary and important to reduce the loss caused by infectious disease. However, it is a challenging work, since the outbreak of infectious disease is uncertain and unclear. Moreover, the detection should be efficient otherwise the best control and prevention time may be missed. To deal with this problem, we propose a geography high-risk zones detection method by capturing the significant change in the infectious disease monitoring data. The main contribution of this paper includes: (1) Analyzing the challenges of the early warning and detection of infectious disease outbreak; (2) Proposing a method to detect the zone that the number of monitoring cases changes significantly; (3) Defining the infection perturbation to describe the infection probability between two zones; (4) Designing an algorithm to measure the infection perturbation of infectious disease between adjacent zones; (5) Performing extensive experiments on both real-world data and synthetic data to demonstrate the effectiveness and efficiency of the proposed methods.

Keywords:Data Mining; Change Mining; Spatial Mining; Time Series

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