This paper provides statistical guidance on the development and application of model-based geostatistical methods for disease prevalence mapping. We illustrate the different stages of the analysis, from exploratory analysis to spatial prediction of prevalence, through a case study on malaria mapping in Tanzania. Throughout the paper, we distinguish between predictive modelling, whose main focus is on maximizing the predictive accuracy of the model, and explanatory modelling, where greater emphasis is placed on understanding the relationships between the health outcome and risk factors. We demonstrate that these two paradigms can result in different modelling choices. We also propose a simple approach for detecting over-fitting based on inspection of the correlation matrix of the estimators of the regression coefficients. To enhance the interpretability of geostatistical models, we introduce the concept of domain effects in order to assist variable selection and model validation. The statistical ideas and principles illustrated here in the specific context of disease prevalence mapping are more widely applicable to any regression model for the analysis of epidemiological outcomes but are particularly relevant to geostatistical models, for which the separation between fixed and random effects can be ambiguous.

本文为基于模型的地统计方法的疾病流行绘图的开发和应用提供了统计指导。我们通过坦桑尼亚疟疾绘图的案例研究来说明分析的不同阶段，从探索性分析到流行率的空间预测。在整篇论文中，我们区分了预测建模和解释性建模，后者的主要重点是最大限度地提高模型的预测准确性，后者更加强调理解健康结果与风险因素之间的关系。我们证明这两种范式可以导致不同的建模选择。我们还提出了一种基于检查回归系数估计量的相关矩阵来检测过拟合的简单方法。为了增强地统计模型的可解释性，我们引入了领域效应的概念，以协助变量选择和模型验证。此处在疾病流行绘图的特定背景下说明的统计思想和原则更广泛地适用于任何用于分析流行病学结果的回归模型，但与地统计模型尤其相关，因为固定效应和随机效应之间的分离可能不明确。