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Irregular Spatial Cluster Detection Based on H1N1 Flu Simulation in Beijing

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Modeling, Design and Simulation of Systems (AsiaSim 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 751))

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Spatial cluster detection of infected areas is widely used for disease surveillance, prevention and containment. However, the commonly used cluster methods cannot resolve the conflicts between the accuracy and efficiency of detection. We present an improved method for flexibly shaped spatial scanning, which can identify Irregular spatial clusters much more accurately and efficiently. First, we convert geographic information to a graph structure. Next, we approximately locate the disease regions. And then, based on the approximately located regions, we detect arbitrarily shaped and connected clusters in the graph based on likelihood ratio. Finally, we check the significance of the identified regions by Monte Carlo method. The algorithm is tested by an agent based simulation of H1N1 influenza data in Beijing. The results show that compared with the previous spatial scan statistic algorithms, our algorithm performs better in terms of shorter time and higher accuracy.

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The authors wish to acknowledge the support of National Science Foundation of China under grant 71373282.

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Correspondence to Yitong Zhao .

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Zhao, Y., Mei, S., Zhang, W. (2017). Irregular Spatial Cluster Detection Based on H1N1 Flu Simulation in Beijing. In: Mohamed Ali, M., Wahid, H., Mohd Subha, N., Sahlan, S., Md. Yunus, M., Wahap, A. (eds) Modeling, Design and Simulation of Systems. AsiaSim 2017. Communications in Computer and Information Science, vol 751. Springer, Singapore.

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  • Print ISBN: 978-981-10-6462-3

  • Online ISBN: 978-981-10-6463-0

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