“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

山东大学学报(医学版)

“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

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

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

收稿日期:2008-06-11 修回日期:1900-01-01 出版日期:2008-11-16 发布日期:2008-11-16
通讯作者: 薛付忠

Study on methodology and application of spatial heterogeneity of disease boundaries

TANG Fang^1,2, XUE Hao³, WANG Zhi-qiang⁴, KANG Dian-min⁴, WANG Yi⁵, XUE Fu-zhong^1,5, WANG Jie-zhen^1,5

1. Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan 250012, China;
2. Qianfoshan Hospital of Shandong Province, Jinan 250014, China;
3. School of Medicine, Shandong University, Jinan 250012, China;
4. Shandong Center for Disease Control and Prevention, Jinan 250014, China;
5. Fudan universityShandong university joint lab of geographical epidemiology and geography, Jinan 250012, China

Received:2008-06-11 Revised:1900-01-01 Online:2008-11-16 Published:2008-11-16
Contact: XUE Fu-zhong

摘要/Abstract

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。

关键词: 地理界限, Monmonier′s Algorithm, 疾病空间结构异质性, 自然疫源性疾病

Abstract: To explore “Monmonier′s algorithm” and its application on spatial structures of natural focal disease. MethodsThe improved Monmonier′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 boundaries of Hemorrhagic fever with renal syndrome (HFRS) were identified. Results The improved Monmonier′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 boundary.

Key words: Spatial heterogeneity of disease, Geographic boundary, Monmonier′s algorithm

中图分类号:

R181.2

唐芳,薛皓,王志强,康殿民,王一,薛付忠,王洁贞,. “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[J]. 山东大学学报(医学版), 2008, 46(11): 1105-1109.

TANG Fang,XUE Hao,WANG Zhi-qiang,KANG Dian-min,WANG Yi,XUE Fu-zhong,WANG Jie-zhen,. Study on methodology and application of spatial heterogeneity of disease boundaries[J]. JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES), 2008, 46(11): 1105-1109.

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