Aghaali Mohammad, Kavousi Amir, Shahsavani Abbas, Hashemi Nazari Seyed Saeed
Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Workplace Health Promotion Research Center, Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Transbound Emerg Dis. 2020 Sep;67(5):2183-2189. doi: 10.1111/tbed.13570. Epub 2020 Apr 28.
Early warning for Infectious disease outbreak is an important public health policy concern, and finding a reliable method for early warning remains one of the active fields for researchers. The purpose of this study was to evaluate the performance of the Bayesian outbreak detection algorithm in the surveillance of influenza-like illness in small regions. The Bayesian outbreak detection algorithm (BODA) and modified cumulative sum control chart algorithm (CUSUM) were applied to daily counts of influenza-like illness in Tehran, Iran. We used data from September 2016 through August 2017 to provide background counts for the algorithms, and data from September 2017 through August 2018 used for testing the algorithms. The performances of the BODA and modified CUSUM algorithms were compared with the results coming from experts' signal inspections. The data of syndromic surveillance of influenza-like illness in Tehran had a median daily counts of 7 (IQR = 3-14). The data showed significant seasonal trends and holiday and day-of-the-week effects. The utility of the BODA algorithm in real-time detection of the influenza outbreak was better than the modified CUSUM algorithm. Moreover, the best performance was when a trend included in the analysis. The BODA algorithm was able to detect the influenza outbreaks with 4-5 days delay, with the least false-positive alarm. Applying the BODA algorithm as an outbreak detection method in influenza-like syndromic surveillance might be useful in early detection of the outbreaks in small regions.
传染病爆发的早期预警是公共卫生政策关注的重要问题，寻找一种可靠的早期预警方法仍然是研究人员活跃的领域之一。本研究的目的是评估贝叶斯爆发检测算法在小区域流感样疾病监测中的性能。将贝叶斯爆发检测算法（BODA）和改良累积和控制图算法（CUSUM）应用于伊朗德黑兰流感样疾病的每日计数。我们使用2016年9月至2017年8月的数据为算法提供背景计数，并使用2017年9月至2018年8月的数据测试算法。将BODA和改良CUSUM算法的性能与专家信号检查的结果进行比较。德黑兰流感样疾病症状监测的数据每日计数中位数为7（四分位间距=3-14）。数据显示出明显的季节性趋势以及节假日和星期效应。BODA算法在实时检测流感爆发方面的效用优于改良CUSUM算法。此外，当分析中包含趋势时性能最佳。BODA算法能够在延迟4-5天的情况下检测到流感爆发，误报最少。将BODA算法作为流感样症状监测中的爆发检测方法应用，可能有助于小区域爆发的早期检测。
