Fang Li-qun, Cao Wu-chun, Chen Hua-xin, Wang Bao-guang, Wu Xiao-ming, Yang Hong, Zhang Xi-tan
Institute of Microbiology and Epidemiology, Academy of Military Medical Science, Beijing 100071, China.
Zhonghua Liu Xing Bing Xue Za Zhi. 2003 Apr;24(4):265-8.
To analyze the spatial distribution of hemorrhagic fever with renal syndrome (HFRS) in China by geographic information system, and to draw up a map on HRFS risk areas.
A set of database was set up using the information collected and linked to electronic maps of China in a software ArcGIS 8.01 from 41 HFRS surveillance sites during 1995 - 1998. A HFRS spatial distribution model was developed using inverse distance weighted interpolation of ArcGIS's spatial analysis method. The normalized difference vegetation index (NDVI) in each HFRS surveillance site was extracted from SPOT4 satellite vegetation imagery. Correlation analysis was performed through SPSS 10.0 to analyze the association between NDVI and HFRS incidence, HFRS risk areas were mapped under different colors.
Spatial distribution model from HFRS surveillance sites showed that HFRS foci mainly presented in the Heilongjiang River drainage, the middle and lower reaches of the Yellow River, the middle and lower reaches of the Yangtze River, and the Jinghang grant Canal-Huaihe River drainage. It was consistent with HFRS distribution map derived from national infectious disease reporting system. Correlation analysis indicated that HFRS incidence rates were significantly associated with NDVI (r = 0.417, P < 0.01). The HFRS risk areas was mapped according to NDVI of each surveillance site.
It is promising to apply GIS technology in predication of the distribution of HFRS by establishing this prediction model.
运用地理信息系统分析中国肾综合征出血热(HFRS)的空间分布,并绘制HFRS风险区域图。
利用1995 - 1998年期间41个HFRS监测点收集的信息建立数据库,并在ArcGIS 8.01软件中与中国电子地图相链接。采用ArcGIS空间分析方法中的反距离加权插值法建立HFRS空间分布模型。从SPOT4卫星植被图像中提取各HFRS监测点的归一化植被指数(NDVI)。通过SPSS 10.0进行相关性分析,以分析NDVI与HFRS发病率之间的关联,用不同颜色绘制HFRS风险区域图。
HFRS监测点的空间分布模型显示,HFRS疫源地主要分布在黑龙江流域、黄河中下游、长江中下游以及京杭大运河 - 淮河流域。这与国家传染病报告系统得出的HFRS分布图一致。相关性分析表明,HFRS发病率与NDVI显著相关(r = 0.417,P < 0.01)。根据各监测点的NDVI绘制了HFRS风险区域图。
通过建立该预测模型,将GIS技术应用于HFRS分布预测具有广阔前景。