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加强公共卫生监测:伊拉克阿尔巴尼亚大规模集会期间用于症状监测的疫情检测算法

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

作者信息

Suraifi Mustafa, Delpisheh Ali, Karami Manoochehr, Mehrabi Yadollah, Jahangiri Katayoun, Lami Faris

机构信息

Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, IRN.

Department of Epidemiology, Safety Promotion and Injury Prevention Research Center, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, IRN.

出版信息

Cureus. 2024 May 12;16(5):e60134. doi: 10.7759/cureus.60134. eCollection 2024 May.

Abstract

BACKGROUND

Large gatherings often involve extended and intimate contact among individuals, creating environments conducive to the spread of infectious diseases. Despite this, there is limited research utilizing outbreak detection algorithms to analyze real syndrome data from such events. This study sought to address this gap by examining the implementation and efficacy of outbreak detection algorithms for syndromic surveillance during mass gatherings in Iraq.

METHODS

For the study, 10 data collectors conducted field data collection over 10 days from August 25, 2023, to September 3, 2023. Data were gathered from 10 healthcare clinics situated along Ya Hussein Road, a major route from Najaf to Karbala in Iraq. Various outbreak detection algorithms, such as moving average, cumulative sum, and exponentially weighted moving average, were applied to analyze the reported syndromes.

RESULTS

During the 10 days from August 25, 2023, to September 3, 2023, 12202 pilgrims visited 10 health clinics along a route in Iraq. Most pilgrims were between 20 and 59 years old (77.4%, n=9444), with more than half being foreigners (58.1%, n=7092). Among the pilgrims, 40.5% (n=4938) exhibited syndromes, with influenza-like illness (ILI) being the most common (48.8%, n=2411). Other prevalent syndromes included food poisoning (21.2%, n=1048), heatstroke (17.7%, n=875), febrile rash (9.0%, n=446), and gastroenteritis (3.2%, n=158). The cumulative sum (CUSUM) algorithm was more effective than exponentially weighted moving average (EWMA) and moving average (MA) algorithms for detecting small shifts.

CONCLUSION

Effective public health surveillance systems are crucial during mass gatherings to swiftly identify and address emerging health risks. Utilizing advanced algorithms and real-time data analysis can empower authorities to improve their readiness and response capacity, thereby ensuring the protection of public health during these gatherings.

摘要

背景

大型集会通常涉及个体之间的长时间密切接触,从而营造出有利于传染病传播的环境。尽管如此,利用疫情检测算法分析此类活动中真实综合征数据的研究却很有限。本研究旨在通过考察伊拉克大规模集会期间症状监测疫情检测算法的实施情况和效果来填补这一空白。

方法

在本研究中,10名数据收集员于2023年8月25日至2023年9月3日的10天内进行了现场数据收集。数据来自伊拉克纳杰夫至卡尔巴拉的主要道路亚侯赛因路沿线的10家医疗诊所。应用了各种疫情检测算法,如移动平均法、累积求和法和指数加权移动平均法,来分析报告的综合征。

结果

在2023年8月25日至2023年9月3日的10天里,12202名朝圣者前往伊拉克某路线沿线的10家健康诊所。大多数朝圣者年龄在20至59岁之间(77.4%,n = 9444),其中一半以上是外国人(58.1%,n = 7092)。在朝圣者中,40.5%(n = 4938)表现出综合征,其中流感样疾病(ILI)最为常见(48.8%,n = 2411)。其他常见综合征包括食物中毒(21.2%,n = 1048)、中暑(17.7%,n = 875)、发热性皮疹(9.0%,n = 446)和肠胃炎(3.2%,n = 158)。累积求和(CUSUM)算法在检测小的变化方面比指数加权移动平均(EWMA)和移动平均(MA)算法更有效。

结论

在大规模集会期间,有效的公共卫生监测系统对于迅速识别和应对新出现的健康风险至关重要。利用先进算法和实时数据分析可以使当局提高其准备和应对能力,从而确保在这些集会期间保护公众健康。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3019/11088799/f89f77e72b91/cureus-0016-00000060134-i01.jpg

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