Mapping malaria risk in West Africa using a Bayesian nonparametric non-stationary model
Mapping malaria risk in West Africa using a Bayesian nonparametric non-stationary model
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DOI：
10.1016/j.csda.2009.02.022
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发表时间：
2009-07-01
影响因子：
1.8
通讯作者：
Smith, T.
中科院分区：
文献类型：
--
作者：
Gosoniu, L.;Vounatsou, P.;Smith, T.
疟疾传播受到环境和气候条件的高度影响，但它们的影响往往不是线性的。在不同农业生态区覆盖的大片地区，气候与疟疾的关系不太可能相同。同样，疟疾传播的空间相关性主要是由空间结构协变量（环境和人为因素）引起的，这些协变量在不同的农业生态区可能会有所不同，从而引入非平稳性。从“绘制非洲疟疾风险”数据库中提取的西非疟疾流行数据进行了分析，以生成区域寄生虫病风险图。建立了一个非平稳地统计模型，假设底层空间过程是每个带内独立平稳过程的混合。在每个农业生态区，用单独的p样条曲线来模拟环境效应的非线性。该模型允许在区域之间的边界处进行平滑处理。作为非线性建模的一种替代方法，p样条方法比协变量分类具有更好的预测能力。模型拟合和预测在贝叶斯框架内处理，使用马尔可夫链蒙特卡罗（MCMC）模拟。(C) 2009 Elsevier B.V.版权所有
Malaria transmission is highly influenced by environmental and climatic conditions but their effects are often not linear. The climate-malaria relation is unlikely to be the same over large areas covered by different agro-ecological zones. Similarly, spatial correlation in malaria transmission arisen mainly due to spatially structured covariates (environmental and human made factors), could vary across the agro-ecological zones, introducing non-stationarity. Malaria prevalence data from West Africa extracted from the "Mapping Malaria Risk in Africa" database were analyzed to produce regional parasitaemia risk maps. A non-stationary geostatistical model was developed assuming that the underlying spatial process is a mixture of separate stationary processes within each zone. Non-linearity in the environmental effects was modeled by separate P-splines in each agro-ecological zone. The model allows smoothing at the borders between the zones. The P-splines approach has better predictive ability than categorizing the covariates as an alternative of modeling non-linearity. Model fit and prediction was handled within a Bayesian framework, using Markov chain Monte Carlo (MCMC) simulations. (C) 2009 Elsevier B.V. All rights reserved.