食源性疾病暴发事件的多尺度空间预警分析

Multi-scale Spatial Early Warning Analysis of Foodborne Disease Outbreaks

以2016年京津冀地区食源性疾病暴发事件暴发数据为例,探索其空间分布规律,为食源性疾病精确预警提供科学依据;在多尺度背景下,采用空间可视化、核密度估计及区域分析等方法对食源性疾病暴发事件进行预警分析。预警分析结果表明,京津冀地区3-12月均有食源性疾病暴发事件,8月份发病人数最多;食源性疾病热点区域主要位于天津市、秦皇岛等临近渤海地区。京津冀地区食源性疾病暴发事件在时间分布上呈现季节性,夏季为高发季节;在空间分布上具有显著的聚集性,东部沿海地区为高发区,南北部为低发区。核密度估计法能够准确刻画食源性疾病暴发事件的空间分布特征,有利于进行热点预警。

Taking the foodborne diseases outbreaks in Beijing-Tianjin-Hebei region in 2016 as an example,the spatial distribution pattern was explored to provide scientific basis for accurate early warning of foodborne diseases outbreaks.In the multi-scale background,spatial visualization,kernel density estimation and regional analysis were used to analyze the foodborne disease outbreaks.The results of early warning analysis showed that there were outbreaks of foodborne diseases in Beijing,Tianjin and Hebei from March to December,with the largest number of cases in August.The hot spots of foodborne diseases were mainly located in Tianjin,Qinhuangdao and other neighboring Bohai area.The incidence of foodborne diseases in Beijing-Tianjin-Hebei region is seasonal,the summer is high.The spatial distribution is significant,and the coastal area is a low incidence area.The kernel density estimation method can accurately depict the spatial distribution characteristics of foodborne disease events and is favorable for hot warning.