Lopes Antunes Ana Carolina, Jensen Dan, Halasa Tariq, Toft Nils
Division for Diagnostics and Scientific Advice-Epidemiology, National Veterinary Institute-DTU, Bülowsvej 27, Frederiksberg C, Denmark.
Section for Production and Health, Department of Large Animal Science-KU, Grønnegårdsvej 8, Frederiksberg C, Denmark.
PLoS One. 2017 Mar 6;12(3):e0173099. doi: 10.1371/journal.pone.0173099. eCollection 2017.
Disease monitoring and surveillance play a crucial role in control and eradication programs, as it is important to track implemented strategies in order to reduce and/or eliminate a specific disease. The objectives of this study were to assess the performance of different statistical monitoring methods for endemic disease control program scenarios, and to explore what impact of variation (noise) in the data had on the performance of these monitoring methods. We simulated 16 different scenarios of changes in weekly sero-prevalence. The changes included different combinations of increases, decreases and constant sero-prevalence levels (referred as events). Two space-state models were used to model the time series, and different statistical monitoring methods (such as univariate process control algorithms-Shewart Control Chart, Tabular Cumulative Sums, and the V-mask- and monitoring of the trend component-based on 99% confidence intervals and the trend sign) were tested. Performance was evaluated based on the number of iterations in which an alarm was raised for a given week after the changes were introduced. Results revealed that the Shewhart Control Chart was better at detecting increases over decreases in sero-prevalence, whereas the opposite was observed for the Tabular Cumulative Sums. The trend-based methods detected the first event well, but performance was poorer when adapting to several consecutive events. The V-Mask method seemed to perform most consistently, and the impact of noise in the baseline was greater for the Shewhart Control Chart and Tabular Cumulative Sums than for the V-Mask and trend-based methods. The performance of the different statistical monitoring methods varied when monitoring increases and decreases in disease sero-prevalence. Combining two of more methods might improve the potential scope of surveillance systems, allowing them to fulfill different objectives due to their complementary advantages.
疾病监测在疾病控制和根除计划中发挥着至关重要的作用，因为跟踪已实施的策略对于减少和/或消除特定疾病非常重要。本研究的目的是评估不同统计监测方法在地方病控制计划场景中的性能，并探讨数据中的变异（噪声）对这些监测方法性能的影响。我们模拟了16种不同的每周血清流行率变化场景。这些变化包括血清流行率上升、下降和保持不变的不同组合（称为事件）。使用两种状态空间模型对时间序列进行建模，并测试了不同的统计监测方法（如单变量过程控制算法——休哈特控制图、表格累积和、V型掩码以及基于99%置信区间和趋势符号的趋势成分监测）。根据引入变化后给定周发出警报的迭代次数来评估性能。结果表明，休哈特控制图在检测血清流行率上升方面比下降方面表现更好，而表格累积和则相反。基于趋势的方法能很好地检测到第一个事件，但在适应多个连续事件时性能较差。V型掩码方法似乎表现最为稳定，与V型掩码和基于趋势的方法相比，基线噪声对休哈特控制图和表格累积和的影响更大。在监测疾病血清流行率的上升和下降时，不同统计监测方法的性能有所不同。结合两种或更多方法可能会扩大监测系统的潜在范围，由于它们的互补优势，使其能够实现不同的目标。
