Geostatistical Methods for Disease Mapping and Visualisation Using Data from Spatio-temporally Referenced Prevalence Surveys
Geostatistical Methods for Disease Mapping and Visualisation Using Data from Spatio-temporally Referenced Prevalence Surveys
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DOI：
10.1111/insr.12268
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发表时间：
2018-12-01
影响因子：
2
通讯作者：
Noor, Abdisalan M.
中科院分区：
文献类型：
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作者：
Giorgi, Emanuele;Diggle, Peter J.;Noor, Abdisalan M.
在这篇文章中，我们制定了一般原则，并发展了地统计学方法来分析时空参照的患病率调查数据。我们的目标是提供一个教程指南，可以用来识别流行地图的简约地统计学模型。提出了一种通用的基于变异函数的蒙特卡罗方法来检验模型假设的有效性。我们描述和对比了基于似然推理和贝叶斯推理的方法，展示了如何在这两种范例下解释参数不确定性。我们还描述了当数据不支持时空协方差函数的平稳性时可以使用的疾病流行标准模型的扩展。我们讨论了如何定义预测目标，并认为超越概率提供了传达流行率估计中不确定性的最有效方式之一。我们描述了用于通过交互动画可视化流行的时空预测性总结的统计软件。最后，我们举例说明了对1905年至2014年在塞内加尔进行的1334项调查的历史疟疾流行数据的应用。
In this paper, we set out general principles and develop geostatistical methods for the analysis of data from spatio-temporally referenced prevalence surveys. Our objective is to provide a tutorial guide that can be used in order to identify parsimonious geostatistical models for prevalence mapping. A general variogram-based Monte Carlo procedure is proposed to check the validity of the modelling assumptions. We describe and contrast likelihood-based and Bayesian methods of inference, showing how to account for parameter uncertainty under each of the two paradigms. We also describe extensions of the standard model for disease prevalence that can be used when stationarity of the spatio-temporal covariance function is not supported by the data. We discuss how to define predictive targets and argue that exceedance probabilities provide one of the most effective ways to convey uncertainty in prevalence estimates. We describe statistical software for the visualisation of spatio-temporal predictive summaries of prevalence through interactive animations. Finally, we illustrate an application to historical malaria prevalence data from 1 334 surveys conducted in Senegal between 1905 and 2014.