Identification of high-risk regions for schistosomiasis in the Guichi region of China: an adaptive kernel density estimation-based approach
Identification of high-risk regions for schistosomiasis in the Guichi region of China: an adaptive kernel density estimation-based approach
复制标题
中国贵池地区血吸虫病高危区识别：基于自适应核密度估计的方法
DOI：
10.1017/s0031182013000048
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发表时间：
2013-03
期刊：
影响因子：
2.4
通讯作者：
张志杰
中科院分区：
文献类型：
--
作者：
张志杰
结论血吸虫病高危区的识别对于合理配置资源和制定有效的防治策略具有重要意义。首次将基于自适应核密度估计（KDE）的空间相对风险函数（sRRF）应用于中国贵池地区血吸虫病高发区的检测，并与基于固定KDE的sRRF进行比较。我们发现，自适应KDE为基础的sRRF有更好的能力来描绘风险区域的异质性，但更敏感的改变用户定义的平滑参数。具体而言，带宽对估计的风险值和风险显著性（P值）的影响对于基于自适应KDE的sRRF较高，但对估计的风险变化标准误差（s.e.）与固定的基于KDE的sRRF相比。基于此应用程序的自适应和固定的KDE为基础的sRRF有各自的优点和缺点，这两种方法的联合应用，可以保证最好的识别高风险的疾病的子区域。
SUMMARY Identification of high-risk regions of schistosomiasis is important for rational resource allocation and effective control strategies. We conducted the first study to apply the newly developed method of adaptive kernel density estimation (KDE)-based spatial relative risk function (sRRF) to detect the high-risk regions of schistosomiasis in the Guichi region of China and compared it with the fixed KDE-based sRRF. We found that the adaptive KDE-based sRRF had a better ability to depict the heterogeneity of risk regions, but was more sensitive to altering the user-defined smoothing parameters. Specifically, the impact of bandwidths on the estimated risk value and risk significance (P value) was higher for the adaptive KDE-based sRRF, but lower on the estimated risk variation standard error (s.e.) compared with the fixed KDE-based sRRF. Based on this application the adaptive and fixed KDE-based sRRF have their respective advantages and disadvantages and the joint application of the two approaches can warrant the best possible identification of high-risk subregions of diseases.
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影响因子：
11.8
作者：
Brooker S;Leslie T;Kolaczinski K;Mohsen E;Mehboob N;Saleheen S;Khudonazarov J;Freeman T;Clements A;Rowland M;Kolaczinski J
通讯作者：
Kolaczinski J
DOI：
10.2307/2347366
发表时间：
1985-06
影响因子：
1.6
作者：
P. Diggle
通讯作者：
P. Diggle
影响因子：
1.7
作者：
Hazelton, Martin L.;Davies, Tilman M.
通讯作者：
Davies, Tilman M.
影响因子：
2.6
作者：
Ward, MP;Carpenter, TE
通讯作者：
Carpenter, TE
影响因子：
2
作者：
Davies, Tilman M.;Hazelton, Martin L.
通讯作者：
Hazelton, Martin L.