Summary Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available SAE has a long history and a wide variety of methods have been suggested, from a bewildering range of philosophical standpoints We describe design-based and model-based approaches and models that are specified at the area level and at the unit level, focusing on health applications and fully Bayesian spatial models The use of auxiliary information is a key ingredient for successful inference when response data are sparse, and we discuss a number of approaches that allow the inclusion of covariate data SAE for HIV prevalence, using data collected from a Demographic Health Survey in Malawi in 2015?2016, is used to illustrate a number of techniques The potential use of SAE techniques for outcomes related to coronavirus disease 2019 is discussed

中文翻译：

---

疾病流行率制图的小面积估计

总结 小区域估计 (SAE) 需要估计领域的兴趣特征，通常是地理区域，其中可能有很少或没有可用样本 SAE 具有悠久的历史，并且从令人眼花缭乱的哲学范围内提出了各种各样的方法立场 我们描述了在区域级别和单元级别指定的基于设计和基于模型的方法和模型，重点关注健康应用和完全贝叶斯空间模型 辅助信息的使用是响应数据时成功推理的关键因素是稀疏的，我们使用从 2015 年到 2016 年马拉维人口健康调查收集的数据，讨论了一些允许包含 HIV 流行率的协变量数据 SAE 的方法，用于说明多种技术 讨论了 SAE 技术对 2019 年冠状病毒病相关结果的潜在用途