Enhancing Public Health Surveillance: Outbreak Detection Algorithms Deployed for Syndromic Surveillance during Arbaeenia Mass Gatherings in Iraq

Background Mass gatherings frequently include close, prolonged interactions between people, which present opportunities for infectious disease transmission. Few published studies have used outbreak detection algorithm methods for real syndrome data collected during mass gatherings. This study aimed to describe the implementation and effectiveness of outbreak detection algorithms for syndromic surveillance during mass gatherings in Iraq. Methods The field data collection involved the participation of 10 data collectors, who carried out the data collection activities over ten days, specifically from August 25, 2023, to September 3, 2023. The data were obtained from 10 healthcare clinics along the major route from Najaf to Karbala, specifically on Ya Hussein Road. The numbers of syndromes reported by applied outbreak detection algorithms include moving average (MA), cumulative sum (CUSUM), and exponential weighted moving average (EWMA). Results A total of 12,202 pilgrims (49.5% females and 50.5% males) visited the 10 health clinics over 10 days from 25 Aug 2023 to 03 Sep 2023. More than three-quarters of the pilgrims (77.4%, n = 9,444), were between the ages of 20 and 59. More than half of the pilgrims were foreigners, accounting for 58.1% (n = 7,092) of the total, and approximately 41.9% (n = 5,110) originated from Iraq. Of those, 40.5% (n = 4,938) had syndromes, 48.8% (n = 2411) had ILI syndromes, 21.2% (n = 1048) had food poisoning syndrome, 17.7% (n = 875) had heatstroke syndrome, 9.0% (n = 446) had febrile rash syndrome, and 3.2% (n = 158) had gastroenteritis syndrome. The CUSUM algorithm was preferable for detecting small shifts compared to the EWMA and MA algorithms. Conclusions The importance of robust public health surveillance systems, particularly during mass gatherings, is to promptly detect and respond to emerging health threats. By leveraging advanced algorithms and real-time data analysis, authorities can enhance their preparedness and response capabilities, ultimately safeguarding public health during such events.