Model-Based Geostatistics for Prevalence Mapping in Low-Resource Settings
Model-Based Geostatistics for Prevalence Mapping in Low-Resource Settings
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DOI：
10.1080/01621459.2015.1123158
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发表时间：
2016-09-01
影响因子：
3.7
通讯作者：
Giorgi, Emanuele
中科院分区：
文献类型：
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作者：
Diggle, Peter J.;Giorgi, Emanuele
在资源匮乏的环境中，流行率绘图依赖于从感兴趣区域内有限的、往往在空间上稀疏的一组社区调查中获得的经验性流行率数据，并可能辅之以遥感图像，这些图像可作为环境风险因素的替代物。这类数据的标准地质统计学模型是广义线性混合模型，具有二项式误差分布、逻辑链接以及解释变量和线性预测器中的高斯空间随机过程的组合。在这篇文章中，我们首先回顾与这个标准模型相关的统计方法和软件，然后考虑几种方法的扩展，其发展的动机是特定的应用程序的要求。其中包括：随机调查数据与非随机调查数据相结合的方法，因此可能有偏见，调查;时空扩展;空间结构零通货膨胀。自始至终，我们通过参与一系列非洲公共卫生项目而产生的疾病绘图应用程序来说明这些方法。
In low-resource settings, prevalence mapping relies on empirical prevalence data from a finite, often spatially sparse, set of surveys of communities within the region of interest, possibly supplemented by remotely sensed images that can act as proxies for environmental risk factors. A standard geostatistical model for data of this kind is a generalized linear mixed model with binomial error distribution, logistic link, and a combination of explanatory variables and a Gaussian spatial stochastic process in the linear predictor. In this article, we first review statistical methods and software associated with this standard model, then consider several methodological extensions whose development has been motivated by the requirements of specific applications. These include: methods for combining randomized survey data with data from non-randomized, and therefore potentially biased, surveys; spatio-temporal extensions; and spatially structured zero-inflation. Throughout, we illustrate the methods with disease mapping applications that have arisen through our involvement with a range of African public health programs.