评论：“患病率制图的两种文化：小区域估计与基于模型的地统计学”
Comment on: "The Two Cultures of Prevalence Mapping: Small Area Estimation and Model-Based Geostatistics"
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中文总结 AI 辅助
该文是对患病率制图领域两种方法SAE与MBG的评论，指出需从多维度完善二者比较，并强调模拟验证对评估MBG模型性能的重要性。
中文摘要 AI 辅助
小区域估计（SAE）与基于模型的地统计学（MBG）为患病率制图提供了互补方法，其相对优势取决于推断目标及可用数据的特征。我们认为更全面的比较应考虑模型可解释性、流行病学驱动协变量的作用、超越区域水平预测的推断目标、不同空间分区与调查数据的整合，以及特定任务的模型验证。特别地，我们质疑是否应将调查设计变量常规纳入MBG模型，因为其影响可能由可测量的环境与社会经济风险因素介导。我们进一步提出，针对患病率制图操作目标的基于模拟的验证，相比仅采用传统交叉验证，能为模型性能提供更具信息性的评估。
英文摘要
Small Area Estimation (SAE) and Model-Based Geostatistics (MBG) provide complementary approaches to prevalence mapping, with their relative advantages depending on the inferential goals and characteristics of the available data. We argue that a fuller comparison should consider model interpretability, the role of epidemiologically motivated covariates, inferential objectives beyond area-level prediction, integration of data from different spatial partitions and surveys, and task-specific model validation. In particular, we question whether survey design variables should routinely be included in MBG models when their effects may instead be mediated by measurable environmental and socio-economic risk factors. We further argue that simulation-based validation, tailored to the operational objectives of prevalence mapping, can provide a more informative assessment of model performance than conventional cross-validation alone.
