“Monmonier′s algorithm”计算几何学方法在识别自然疫源性疾病空间结构异质性界限中的应用  

Study on methodology and application of spatial heterogeneity of disease boundaries

作　　者：唐芳[1,2] 薛皓[3] 王志强[4] 康殿民[4] 王一[5] 薛付忠[1,5] 王洁贞[1,5] 

机构地区：[1]山东大学公共卫生学院流行病与卫生统计学研究所,济南250012 [2]山东省千佛山医院,济南250014 [3]山东大学医学院,济南250012 [4]山东省疾病预防控制中心,济南25014 [5]复旦大学山东大学地理流行病学与基因地理学联合实验室,济南250012

出　　处：《山东大学学报（医学版）》2008年第11期1105-1109,共5页Journal of Shandong University:Health Sciences

基　　金：国家自然科学基金资助课题(30471489)

摘　　要：目的探讨"Monmonier′s algorithm"计算几何学方法在识别自然疫源性疾病空间结构异质性界限中的应用。方法以自然疫源性疾病肾综合征出血热(HFRS)为例,以疫源地内疫点间相似距离测度矩阵、疫点的空间位置坐标矩阵为基础,在Delaunay三角测量框架内,利用改进的"Monmonier′s algorithm"计算几何学方法构建基于空间点数据的旨在寻找疫点间最大差异的疾病空间结构异质性界限识别模型。结果改进的"Monmonier′s Algorithm"计算几何学方法较好地识别出了疫源地空间结构的地理界限,找出了同质的疾病地理区域的边界或疾病空间化变量变化迅速的地带,且能通过Bootstrap重采样方法检验界限的统计显著性,特别是能够展示地理界限的层次性和空间邻接性。结论改进的"Monmonier′s Algorithm"计算几何学方法是识别疾病空间结构异质性界限的良好方法。Objective To explore ＂Monmonier＇s algorithm＂ and its application on spatial structures of natural focal disease. Methods The improved Momnonier＇s algorithm model based on Monmonier＇s maximum-distance algorithm and distance matrix between the corresponding data, which were connected using a Delaunay triangulation, was built. Structures and bonndaries of Hemorrhagic fever with renal syndrome （HFRS） were identified. Resulls The improved Moumonier＇ s algorithm model well showed the edges associated with the highest rate of changes in the given distance measure, namely the areas where differences between locations of disease are largest. The bootstrap test was used to assess the robustness of computed boundaries and infer the spatial connection and arrangement of boundaries. Conclusion The improved Monmonier＇ s algorithm model is useful in detecting spatial heterogeneity of disease boundaries.

关 键 词：自然疫源性疾病 疾病空间结构异质性 地理界限 Monmonier′sAlgorithm 

分 类 号：R181.2[医药卫生—流行病学]