Department of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Department of Statistics, Lorestan University, Khorramabad, Iran.
BMC Med Res Methodol. 2023 Oct 14;23(1):235. doi: 10.1186/s12874-023-02050-z.
Public health surveillance serves a crucial function within health systems, enabling the monitoring, early detection, and warning of infectious diseases. Recently, outbreak detection algorithms have gained significant importance across various surveillance systems, particularly in light of the COVID-19 pandemic. These algorithms are approached from both theoretical and practical perspectives. The theoretical aspect entails the development and introduction of novel statistical methods that capture the interest of statisticians. In contrast, the practical aspect involves designing outbreak detection systems and employing diverse methodologies for monitoring syndromes, thus drawing the attention of epidemiologists and health managers. Over the past three decades, considerable efforts have been made in the field of surveillance, resulting in valuable publications that introduce new statistical methods and compare their performance. The generalized linear model (GLM) family has undergone various advancements in comparison to other statistical methods and models. This study aims to present and describe GLM-based methods, providing a coherent comparison between them. Initially, a historical overview of outbreak detection algorithms based on the GLM family is provided, highlighting commonly used methods. Furthermore, real data from Measles and COVID-19 are utilized to demonstrate examples of these methods. This study will be useful for researchers in both theoretical and practical aspects of outbreak detection methods, enabling them to familiarize themselves with the key techniques within the GLM family and facilitate comparisons, particularly for those with limited mathematical expertise.
公共卫生监测在卫生系统中起着至关重要的作用，能够监测、早期发现和预警传染病。最近，爆发检测算法在各种监测系统中变得越来越重要，特别是在 COVID-19 大流行期间。这些算法从理论和实践两个方面进行了研究。理论方面涉及开发和引入新的统计方法，这些方法引起了统计学家的兴趣。相比之下，实践方面涉及设计爆发检测系统和采用不同的方法来监测症状，从而引起了流行病学家和卫生管理人员的关注。在过去的三十年中，监测领域做出了巨大的努力，产生了有价值的出版物，介绍了新的统计方法并比较了它们的性能。与其他统计方法和模型相比，广义线性模型（GLM）家族已经有了很大的改进。本研究旨在介绍和描述基于 GLM 的方法，并对它们进行一致的比较。首先，提供了基于 GLM 家族的爆发检测算法的历史概述，突出了常用的方法。此外，还利用麻疹和 COVID-19 的真实数据来演示这些方法的实例。本研究将对爆发检测方法的理论和实践方面的研究人员有用，使他们熟悉 GLM 家族中的关键技术，并促进比较，特别是对那些数学专业知识有限的人员。
