Simulation Analysis of Beijing H1N1 Influenza Based on Spatial Clustering

doi:10.16182/j.issn1004731x.joss.201709032

Abstract

Abstract: Spatial cluster detection is widely used for disease surveillance, prevention and containment. In the early stages of illness, epidemics have similar symptoms to common diseases, making infectious disease data processing and analysis difficult. The Agent based modeling and simulation was used to generate H1N1 influenza data in Beijing. By designing a set of experiments, the epidemic monitoring results of two spatial clustering algorithms were analyzed. The results show that, using spatial clustering algorithms to analyze the simulation data of the epidemics can help to reveal the spread of the epidemic and play a positive role in the surveillance and prevention of infectious diseases.

Key words: agent-based modeling and simulation, disease surveillance, spatial clustering, statistics and analysis

CLC Number:

Zhao Yitong, Mei Shan, Ma Liang, Zhang Wei. Simulation Analysis of Beijing H1N1 Influenza Based on Spatial Clustering[J]. Journal of System Simulation, 2017, 29(9): 2115-2121.

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