Performance of Bayesian outbreak detection algorithm in the syndromic surveillance of influenza-like illness in small region.

Abstract

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 4-5days 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.