Giorgi Emanuele, Diggle Peter J, Snow Robert W, Noor Abdisalan M
Lancaster Medical School, Lancaster University, Lancaster, UK.
Population and Health Theme, Kenya Medical Research Institute - Wellcome Trust Research Programme, Nairobi, Kenya.
Int Stat Rev. 2018 Dec;86(3):571-597. doi: 10.1111/insr.12268. Epub 2018 Apr 25.
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.
在本文中，我们阐述了一般原则，并开发了地统计学方法来分析时空参照患病率调查的数据。我们的目标是提供一份教程指南，可用于识别用于患病率制图的简约地统计学模型。提出了一种基于通用变差函数的蒙特卡罗程序来检验建模假设的有效性。我们描述并对比了基于似然和贝叶斯的推断方法，展示了在两种范式下如何考虑参数不确定性。我们还描述了疾病患病率标准模型的扩展，当数据不支持时空协方差函数的平稳性时可使用这些扩展。我们讨论了如何定义预测目标，并认为超越概率是传达患病率估计不确定性的最有效方法之一。我们描述了用于通过交互式动画可视化患病率时空预测总结的统计软件。最后，我们举例说明了对1905年至2014年在塞内加尔进行的1334次调查的历史疟疾患病率数据的应用。
