基于空间聚类的北京H1N1流感仿真分析

发布时间：2018-03-31 10:15

  本文选题：基于Agent建模与仿真　切入点：传染病监测　出处：《系统仿真学报》2017年09期

【摘要】：空间聚类广泛用于传染病的监测、预防和控制。传染病与普通疾病在早期具有相似的症状,使得传染病数据处理和分析更为困难。采用基于Agent的仿真建模方法,生成北京暴发H1N1流感的仿真数据。基于4组分布形状与规模不同的数据,对2种空间聚类算法的疫情监测结果进行分析。结果表明,通过空间聚类算法对Agent仿真数据进行分析,有助于揭示疫情的扩散规律,进而在传染病监测和防控方面起到积极作用。
[Abstract]:Spatial clustering is widely used in surveillance, prevention and control of infectious diseases. Infectious diseases and common diseases have similar symptoms in the early stage, which makes data processing and analysis of infectious diseases more difficult. The simulation data of Beijing H1N1 influenza outbreak were generated. Based on four groups of data with different distribution shapes and scales, the epidemic monitoring results of two spatial clustering algorithms were analyzed. The results showed that the Agent simulation data were analyzed by spatial clustering algorithm. It is helpful to reveal the law of epidemic spread and play an active role in infectious disease surveillance and prevention and control.
【作者单位】： 国防科技大学信息系统与管理学院;中国空间技术研究院通信卫星事业部;
【基金】：国家自然科学基金(71373282)
【分类号】：R511.7;TP391.9
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本文编号：1690292