Minaeian Sara, Alimohamadi Yousef, Eshrati Babak, Esmaeilzadeh Firooz
Antimicrobial Resistance Research Center, Institute of Immunology & Infectious Diseases Iran University of Medical Sciences Tehran Iran.
Health Research Center, Life Style Institute Baqiyatallah University of Medical Sciences Tehran Iran.
Health Sci Rep. 2023 May 3;6(5):e1245. doi: 10.1002/hsr2.1245. eCollection 2023 May.
Timely detection of outbreaks is one of the main purposes of the health surveillance system. The presence of appropriate methods in the detection of outbreaks can have an important role in the timely detection of outbreaks. Because of the importance of this issue, this study aimed to assess the performance of discrete wavelet transform (DWT) based methods in detecting influenza outbreaks in Iran from January 2010 to January 2020.
All registered influenza-positive virus cases in Iran from January 2010 to January 2010 were obtained from the FluNet web base tool, the World Health Organization website. The combination method that includes DWT and Shewhart control chart was used in this study. All analyses were performed using MATLAB software version 2018a Stata software version 15.
The Mean ± SD and median of reported influenza cases from January 2010 to January 2020 was 36 ± 108 and four cases per week. The combination of the DWT and Shewhart control chart with = 0.25 had the most sensitivity. The most specificity in the detection of nonoutbreak days was seen in the combination of DWT and Shewhart control chart with = 1.5, = 1.75, and = 2, respectively. The combination of DWT and Shewhart control chart with = 0.5 had the best performance in the detection of outbreaks (sensitivity = 0.64, specificity: 0.90, Youden index: 0.54, and area under the curve [AUC]: 0.77).
The DWT-based method in detecting influenza outbreaks has acceptable performance, but it is recommended that this method's performance be assessed in detecting outbreaks of other infectious diseases.
及时发现疫情是健康监测系统的主要目标之一。在疫情检测中采用合适的方法对于及时发现疫情具有重要作用。鉴于此问题的重要性，本研究旨在评估基于离散小波变换（DWT）的方法在检测2010年1月至2020年1月伊朗流感疫情中的性能。
2010年1月至2010年1月伊朗所有登记的流感病毒阳性病例均从世界卫生组织网站的FluNet网络基础工具中获取。本研究使用了包括DWT和休哈特控制图的组合方法。所有分析均使用MATLAB软件版本2018a和Stata软件版本15进行。
2010年1月至2020年1月报告的流感病例的均值±标准差和中位数分别为36±108例和每周4例。DWT与休哈特控制图相结合，当λ = 0.25时灵敏度最高。在检测非疫情日时，DWT与休哈特控制图相结合，当λ分别为1.5、1.75和2时特异性最高。DWT与休哈特控制图相结合，当λ = 0.5时在疫情检测中表现最佳（灵敏度 = 0.64，特异性：0.90，约登指数：0.54，曲线下面积[AUC]：0.77）。
基于DWT的方法在检测流感疫情方面具有可接受的性能，但建议评估该方法在检测其他传染病疫情中的性能。
