Statistical methodological issues in mapping historical schistosomiasis survey data
Statistical methodological issues in mapping historical schistosomiasis survey data
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DOI：
10.1016/j.actatropica.2013.04.012
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发表时间：
2013-11-01
期刊：
影响因子：
2.7
通讯作者：
Vounatsou, Penelope
中科院分区：
文献类型：
--
作者：
Chammartin, Frederique;Huerlimann, Eveline;Vounatsou, Penelope
对于用于控制的资源仍然有限的血吸虫病和其他被忽视的热带病，需要基于模型的地图来确定控制干预措施的空间目标，并监测控制规划。贝叶斯地质统计模型已被广泛而有效地用于生成平滑的经验风险图。在本文中，我们回顾了与血吸虫病风险建模相关的重要问题，包括大数据集的贝叶斯计算，历史调查数据的异质性，平稳和各向同性假设以及贝叶斯地统计变量选择的新方法。我们提供了一个基于科特迪瓦曼氏血吸虫历史流行数据的高级贝叶斯地统计变量选择的例子。我们在回归系数之前包含了一个“参数扩展的正态混合反伽马”，这反过来又允许选择协变量块，特别是分类变量。所实现的贝叶斯地统计变量选择为贝叶斯地统计框架内预测因子的选择提供了一种严格的方法，确定了曼氏链球菌感染风险的最重要预测因子，与忽略数据空间结构的传统选择方法相比，该模型更加简洁。总之，贝叶斯地统计模型的统计进展为解释血吸虫感染的重要固有特征提供了独特的机会，因此贝叶斯地统计模型可以指导控制干预措施的空间靶向。(C) 2013 Elsevier B.V.版权所有
For schistosomiasis and other neglected tropical diseases for which resources for control are still limited, model-based maps are needed for prioritising spatial targeting of control interventions and surveillance of control programmes. Bayesian geostatistical modelling has been widely and effectively used to generate smooth empirical risk maps. In this paper, we review important issues related to the modelling of schistosomiasis risk, including Bayesian computation of large datasets, heterogeneity of historical survey data, stationary and isotropy assumptions and novel approaches for Bayesian geostatistical variable selection. We provide an example of advanced Bayesian geostatistical variable selection based on historical prevalence data of Schistosoma mansoni in Cote d'Ivoire. We include a "parameter expanded normal mixture of inverse-gamma" prior for the regression coefficients, which in turn allows selection of blocks of covariates, particularly categorical variables. The implemented Bayesian geostatistical variable selection provided a rigorous approach for the selection of predictors within a Bayesian geostatistical framework, identified the most important predictors of S. mansoni infection risk and led to a more parsimonious model compared to traditional selection approaches that ignore the spatial structure in the data. In conclusion, statistical advances in Bayesian geostatistical modelling offer unique opportunities to account for important inherent characteristics of the Schistosoma infection, and hence Bayesian geostatistical models can guide the spatial targeting of control interventions. (C) 2013 Elsevier B.V. All rights reserved.