Department of Psychology, 69001Panteion University of Social and Political Sciences, Athens, Greece.
Stat Methods Med Res. 2022 Jun;31(6):1067-1084. doi: 10.1177/09622802221079347. Epub 2022 Feb 15.
Worldwide, the detection of epidemics has been recognized as a continuing problem of crucial importance to public health surveillance. Various approaches for detecting and quantifying epidemics of infectious diseases in the recent literature are directly influenced by methods of Statistical Process Control (SPC). However, implementing SPC quality tools directly to the general health care monitoring problem, in a similar manner as in industrial quality control, is not feasible since many assumptions such as stationarity, known asymptotic distribution etc. are not met. Toward this end, in this paper, some of the open statistical research issues involved in this field are discussed, and a distribution-free control charting technique based on change-point analysis is applied and evaluated for detection of epidemics. The main tool in this methodology is the detection of unusual trends, in the sense that the beginning of an unusual trend marks a switch from a control state to an epidemic state. The in-control and out-of-control performance of the adapted control scheme from SPC is thoroughly investigated using Monte Carlo simulations, and the applied scheme is found to outperform its parametric and nonparametric competitors in many cases. Moreover, the empirical comparative study provides evidence that the adapted change-point detection scheme has several appealing properties compared to the current practice for detection of epidemics.
在全球范围内，传染病的检测一直被认为是公共卫生监测中至关重要的持续问题。最近文献中用于检测和量化传染病流行的各种方法直接受到统计过程控制（SPC）方法的影响。然而，直接将 SPC 质量工具应用于一般医疗保健监测问题，就像在工业质量控制中那样，是不可行的，因为许多假设（如平稳性、已知渐近分布等）不成立。为此，本文讨论了该领域涉及的一些公开的统计研究问题，并应用了一种基于变化点分析的无分布控制图技术来检测传染病。该方法的主要工具是检测异常趋势，因为异常趋势的开始标志着从控制状态到传染病状态的转变。通过蒙特卡罗模拟，彻底研究了从 SPC 适应的控制方案的在控和失控性能，发现该应用方案在许多情况下优于其参数和非参数竞争对手。此外，实证比较研究提供了证据，表明与当前用于检测传染病的方法相比，适应的变化点检测方案具有一些吸引人的特性。
