Zhang Zhi-Ying, Xu De-Zhong, Zhou Xiao-Nong, Zhou Yun, Liu Shi-Jun
Department of Epidemiology, Fourth Military Medical University, Shaanxi Province 710032, and National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China.
Acta Trop. 2005 Nov-Dec;96(2-3):205-12. doi: 10.1016/j.actatropica.2005.07.027. Epub 2005 Sep 16.
Remote sensing and spatial statistical analysis were employed to predict the distribution of Oncomelania hupensis, the intermediate host snail of Schistosoma japonicum, in the marshlands of Jiangning county in China. Surrogate indices related to environmental factors in the marshlands were derived from a Landsat 7 ETM+ image, and the relationship between environmental covariates and the density of O. hupensis was analyzed by stepwise regression models and ordinary kriging. Although stepwise regression demonstrated that O. hupensis densities of live snails in the marshlands related significantly to the modified soil-adjusted vegetation index, wetness and land surface temperature, the correlation coefficient was low (0.282). Therefore, spatial patterns of the regression residual were investigated by the semi-variogram method, and the spatial variation of O. hupensis density attributed to the spatial autocorrelation was estimated by ordinary kriging. The regression model of the snail density and ordinary kriging of its spatial variation were then combined with the aim of improving the prediction of O. hupensis. Following this approach, the prediction indeed improved considerably (0.852). Our results show that it is possible to predict the distribution of O. hupensis in these marshlands by using remotely sensed environmental indices, and that spatial statistical analyses are capable of improving prediction accuracy. These findings are of relevance for mapping and prediction of schistosomiasis japonica in China, and hence the national control programme.
运用遥感和空间统计分析方法预测日本血吸虫中间宿主钉螺在中国江宁地区沼泽地的分布情况。从陆地卫星7 ETM +图像中获取与沼泽地环境因子相关的替代指标，并通过逐步回归模型和普通克里金法分析环境协变量与钉螺密度之间的关系。尽管逐步回归分析表明，沼泽地活螺的钉螺密度与修正的土壤调整植被指数、湿度和地表温度显著相关，但相关系数较低（0.282）。因此，采用半变异函数法研究回归残差的空间格局，并通过普通克里金法估计归因于空间自相关的钉螺密度空间变异。然后将钉螺密度回归模型及其空间变异的普通克里金法相结合，旨在提高对钉螺的预测能力。采用这种方法后，预测结果确实有了显著改善（0.852）。我们的研究结果表明，利用遥感环境指数预测这些沼泽地钉螺的分布是可行的，并且空间统计分析能够提高预测精度。这些发现对于中国日本血吸虫病的测绘和预测以及国家防控计划具有重要意义。
