Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29466, USA.
Spat Spatiotemporal Epidemiol. 2022 Jun;41:100431. doi: 10.1016/j.sste.2021.100431. Epub 2021 May 8.
In this paper I review some of the major issues that arise when geo-referenced health data are to be the subject of prospective surveillance. The review focusses on modelbased approaches to this activity, and proposes the Bayesian paradigm as a convenient vehicle for modeling. Various posterior functional measures are discussed including the SCPO and SKL and a number of extensions to these are considered. Overall the value of Bayesian Hierarchical Modeling (BHM) with surveillance functionals is stressed in its relevance to early warning of adverse risk scenarios.
在本文中，我回顾了在对地理参考健康数据进行前瞻性监测时出现的一些主要问题。审查的重点是针对这种活动的基于模型的方法，并提出贝叶斯范例作为建模的便捷工具。讨论了各种后验函数度量，包括 SCPO 和 SKL，并考虑了这些度量的一些扩展。总体而言，强调了具有监测功能的贝叶斯分层模型（BHM）在对不利风险情况进行早期预警方面的相关性，突出了其价值。
