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

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.

Evaluation of the influenza-like illness surveillance system in Tunisia, 2012-2015.

BACKGROUND: This study was initiated to evaluate, for the first time, the performance and quality of the influenza-like illness (ILI) surveillance system in Tunisia. METHODS: The evaluation covered the period of 2012-2015 and used different data sources to measure indicators related to data quality and completeness, representativeness, timeliness, simplicity, acceptability, flexibility, stability and utility. RESULTS: During the evaluation period, 485.221 ILI cases were reported among 6.386.621 outpatients at 268 ILI sentinel sites. To conserve resources, cases were only enrolled and tested for influenza during times when the number of patients meeting the ILI case definition exceeded 7% (10% after 2014) of the total number of outpatients for the week. When this benchmark was met, five to 10 patients were enrolled and sampled by nasopharyngeal swabs the following week. In total, The National Influenza Center (NIC) received 2476 samples, of which 683 (27.6%) were positive for influenza. The greatest strength of the system was its representativeness and flexibility. The timeliness of the data and the acceptability of the surveillance system performed moderately well; however, the utility of the data and the stability and simplicity of the surveillance system need improvement. Overall, the performance of the Tunisian influenza surveillance system was evaluated as performing moderately well for situational awareness in the country and for collecting representative influenza virologic samples. CONCLUSIONS: The influenza surveillance system in Tunisia provided pertinent evidence for public health interventions related to influenza situational awareness. To better monitor influenza, we propose that ILI surveillance should be limited to sites that are currently performing well and the quality of data collected should be closely monitored and improved.