中国寄生虫学与寄生虫病杂志

• 论著 • 上一篇    下一篇

湖南省洞庭湖区堤坝涵闸对血吸虫感染风险的空间影响分析

朱政1,贺清云2 *   

  1. 1 湖南师范大学美术学院环境艺术系,长沙410081;2 湖南师范大学资源与环境科学学院,长沙410081
  • 出版日期:2015-10-30 发布日期:2016-01-06

Spatial Effect of the Dams and Sluices on the Risk of Schistosomiasis in Dongting Lake Region of Hunan Province

ZHU Zheng1, HE Qing-yun2 *   

  1. 1 College of Fine Art, Hunan Normal University, Changsha 410081,China;2 College of Resource and Environment Science, Hunan Normal University, Changsha 410081,China
  • Online:2015-10-30 Published:2016-01-06

摘要:

目的 分析湖南省洞庭湖区堤坝、涵闸对血吸虫感染风险的空间影响机制、形式与程度。 方法 将湖南省洞庭湖区范围内堤坝、涵闸的位置、等级和类别在数字化的区域地图和遥感地图上进行标注。收集洞庭湖区334个乡镇2010-2014年各年3~7月份人群血吸虫病流行病学资料,计算5年平均感染率。建立1 220个六边形单元格构成的格网系统,对堤坝、涵闸的血吸虫感染风险和人群感染率进行空间量化。利用二步聚类对堤坝、涵闸和人群感染率的格网系统进行分析,确定感染峰值区。以离心模型测算感染峰值区对周边区域的影响深度和广度。以相关系数模型计算堤坝、涵闸与人群血吸虫病感染率空间分布的相关系数。将相关系数转换为权重,计算全部单元格的综合加权感染风险,最终获得洞庭湖区血吸虫感染风险的综合加权分布图。  结果 对湖南省洞庭湖区堤坝、涵闸和人群感染率的格网系统的二步聚类分析的结果显示,共有15个感染峰值区。离心模型测算结果显示,感染峰值区对周边区域的感染风险有显著的提升作用,相邻感染峰值区的感染风险存在叠加效应。堤坝、涵闸与人群感染率的相关系数分别为0.82、0.86,权重分别为0.488 1、0.511 9。高、较高感染风险区沿澧水、西洞庭湖、南洞庭湖、东洞庭湖和城陵矶呈新月状分布。通过绝对平均误差(MAE)检验确定研究结果有效。 结论 湖南省洞庭湖区的堤坝、涵闸与血吸虫感染风险存在强正相关关系。15个感染峰值区对血吸虫病传播有决定性作用,对周边区域的血吸虫感染风险造成显著的空间梯度型影响。

关键词: 血吸虫病, 感染风险, 堤坝, 涵闸, 洞庭湖区

Abstract:

Objective  To determine the spatial effect of the dams and the sluices on the risk of schistosomiasis in Dongting Lake Region, and its mechanism, type and degree. Methods The positions, levels and types of the dams and the sluices in Dongting Lake Region of Hunan Province were marked on the base map. Epidemiological data of schistosomiasis among populations in 334 Townships in the Region were collected during March and July in each year of 2010-2014, and the 5-year average infection rate was calculated. A grid system composed of 1 220 hexagons was built to spatially quantify the effect of the dams and the sluices on the risk of schistosomiasis and the infection rates among populations. A two-step clustering was used to analyze the grid system, and the areas of infection peak were identified. The centrifugal model was used to simulate the degree and scope of the influence of these infection peak areas on the surrounding regions. The correlation coefficient model was used to determine the correlation coefficients between the dams and sluices and the spatial distribution of schistosomiasis. The correlation coefficients were then weighted, the integrated weighted risk of all grid cells calculated, and the weighted distribution figure for schistosomiasis risk produced. Results The results of two-step clustering revealed 15 areas of infection peak in the Region. The results of centrifugal model showed that the areas of infection peak significantly promoted the effect of the surrounding regions on the risk of infection. The correlation coefficients of the dams and the sluices with the infection rate among populations were 0.82 and 0.86, with the weights being 0.488 1 and 0.511 9,  respectively. The areas with high and relatively high risks of infection had a Crescent-shaped distribution along the Lishui River, West Dongting Lake, South Dongting Lake, East Dongting Lake and Chenglingji. The results were further verified by MAE testing. Conclusion The dams and sluices correlated strongly and positively with the risk of schistosomiasis. Fifteen areas of infection peak are identified to be determinant for schistosomiasis infection and spread, and have significant gradient spatial effect on schistosomiasis risk in surrounding areas.

Key words: Schistosomiasis, Infection risk, Levee, Sluice, Dongting Lake