应用地理信息系统研究我国肾综合征出血热疫区类型与主要宿主动物构成的关系

目的 分析中国肾综合征出血热(HFRS)疫区的性质与主要宿主动物种群构成的关系。方法 收集中国122个地区1991～1999年HFRS病例血清分型资料和41个监测点的宿主动物种类及数量资料,在ArcGIS 8.1软件的支持下,建立中国HFRS病例分型地理信息系统(GIS)和监测点宿主动物构成GIS,并以此为基础通过反距离加权插值法(IDW)建立中国HFRS主要疫区分型地图,叠合监测点宿主动物构成图;对各监测点的宿主动物构成进行分层聚类分析。结果 中国HFRS不同类型疫区呈现明显的区域性分布,总体表现为从东北至西南方向Ⅰ型汉坦病毒(HV)为主的混合型疫区与Ⅱ型HV为主的混合型疫区交替分布的特征;监测点野外宿主动物种类较多且各地区鼠种构成差异较大,居民区宿主动物种类相对较少,且优势鼠种大多局限于2～3种宿主动物;监测点宿主动物构成的聚类分析结果显示其疫区类型与所在疫区病例血清型基本一致。结论 以空间数据库为基础的GIS及其空间分析能根据HFRS样点信息很好地预测各主要疫区的分型特征。

Using geographic information system to study on the association between epidemic areas and main animal hosts of hemorrhagic fever with renal syndrome in China

Objective To analyze the association between types of epidemic areas and proportion of main animal hosts of hemorrhagic fever with renal syndrome(HFRS) in China. Methods Data on serotype of HFRS patients from 122 regions and species as well as number of animal hosts from 41 national surveillance sites were collected and managed. The map of the types of epidemic areas from 1991-1999 in China was produced using inverse distance weighted technique in the ArcGIS 8.1 software. The map of the proportion of host animals was then overlapped on it and the surveillance sites of HFRS were clustered in terms of the proportion of host animals. Results Variance in spatial distribution of the types of epidemic areas was obsevred. Two epidemic area types, namely Hantaan-dominant mixed epidemic areas and Souldominant mixed epidemic areas seemed to have transferred from the north-east to south-west of China in turns. There were more species of HFRS animal hosts in fields than those of in residential areas. The diversity of rodent species in fields was higher than that of the residential areas which predominant harbored rodent species of 2-3 kinds. Types of surveillance sites classified by cluster analysis of proportion of main animal hosts were consistent with the serotypes of HFRS patients. Conclusion Through employment of spatial analysis technique based on GIS databases, the HFRS types of main epidemic area in China could be well predicted by surveillance data.