Applying the moving epidemic method to determine influenza epidemic and intensity thresholds using influenza‐like illness surveillance data 2009‐2018 in Tunisia

大流行 流感季节 2019年冠状病毒病(COVID-19) 医学 准备 人口学 流行病模型 严重急性呼吸综合征冠状病毒2型(SARS-CoV-2) 病毒学 环境卫生 兽医学 接种疫苗 流感疫苗 传染病(医学专业) 人口 内科学 疾病 社会学 政治学 法学
作者
Hind Bouguerra,Elyes Boutouria,Mokhtar Zorraga,Amal Cherif,Rihab Yazidi,Naima Abdeddaiem,Latifa Maazaoui,Awatef ElMoussi,Salma Abid,Slim Amine,Leila Bouabid,Souha Bougatef,Mohamed Kouni Chahed,Afif Ben Salah,Jihène Bettaieb,Nissaf Ben Alaya
出处
期刊:Influenza and Other Respiratory Viruses [Wiley]
卷期号:14 (5): 507-514 被引量:5
标识
DOI:10.1111/irv.12748
摘要

Abstract Background Defining the start and assessing the intensity of influenza seasons are essential to ensure timely preventive and control measures and to contribute to the pandemic preparedness. The present study aimed to determine the epidemic and intensity thresholds of influenza season in Tunisia using the moving epidemic method. Methods We applied the moving epidemic method (MEM) using the R Language implementation (package “mem”). We have calculated the epidemic and the different intensity thresholds from historical data of the past nine influenza seasons (2009‐2010 to 2017‐2018) and assessed the impact of the 2009‐2010 pandemic year. Data used were the weekly influenza‐like illness (ILI) proportions compared with all outpatient acute consultations. The goodness of the model was assessed using a cross validation procedure. Results The average duration of influenza epidemic during a typical season was 20 weeks and ranged from 11 weeks (2009‐2010 season) to 23 weeks (2015‐2016 season). The epidemic threshold with the exclusion of the pandemic season was 6.25%. It had a very high sensitivity of 85% and a high specificity of 69%. The different levels of intensity were established as follows: low, if ILI proportion is below 9.74%, medium below 12.05%; high below 13.27%; and very high above this last rate. Conclusions This is the first mathematically based study of seasonal threshold of influenza in Tunisia. As in other studies in different countries, the model has shown both good specificity and sensitivity, which allows timely and accurate detection of the start of influenza seasons. The findings will contribute to the development of more efficient measures for influenza prevention and control.
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