Evaluating tools to define influenza baseline and threshold values using surveillance data, Egypt, season 2016/17. Issue 3 (March 2020)

• Record Type: Journal Article
• Authors: AbdElGawad, Basma; Vega, Tomás; El Houssinie, Moustafa; Mohsen, Amira; Fahim, Manal; Abu ElSood, Hanaa; Jabbour, Jean; Eid, Alaa; Refaey, Samir
• Is Part Of: Journal of infection and public health. Volume 13:Issue 3(2020)
• Page Start: 430
• Page End: 437
• Publication Date: 2020-03
• Subjects: Epidemiology -- Influenza -- Human -- Surveillance -- Egypt; Communicable diseases -- Periodicals; Public health -- Periodicals; Epidemiology -- Periodicals; Nosocomial infections -- Prevention -- Periodicals; Medical microbiology -- Periodicals; 614.4
• DOI: 10.1016/j.jiph.2019.05.005
• Languages: English
• ISSNs: 1876-0341

Abstract

Abstract: Background: Establishing influenza thresholds and transmission intensity can help evaluate seasonal changes in influenza severity and potential pandemics. We aimed to evaluate the moving epidemic method (MEM) for calculating influenza thresholds for season 2016/17 in Egypt using four parameters, to identify the most useful parameter. Also to measure the agreement between both the country-specific statistical empirical method and World Health Organization method to MEM for determining the length and intensity level of activity of the influenza season. Methods: Routinely epidemiological and laboratory data from sentinel surveillance sites for Severe Acute Respiratory Infection (SARI) and influenza-like illness (ILI) were used for calculating thresholds for seasons between 2010/11 and 2015/16 to test 2016/17 season. The parameters calculated were: screened ILI consultation rate × 1000, screened ILI composite parameter, influenza positivity percentage among sampled SARI cases, and influenza positivity percentage among sampled ILI and SARI cases. These parameters assess seasonality and intensity of influenza activity using the three proposed methods (mentioned above). Agreement between the three methods was done using several approaches. Results: The intensity of influenza activity by MEM was lower than the other two methods. Agreement between MEM and each of the other two techniques varied appreciably from good to very good for seasonal duration, and poor to fair for intensity level. In addition, parameters including laboratory data showed a pattern of bi-wave activity; the first wave occurred in winter mostly between epidemiological weeks 39 and 52 and the second occurred in spring mostly between weeks 12 and 17. Conclusion: Parameters including laboratory data were more useful in defining seasonality of influenza. Further exploration of the MEM model in future seasons may help to provide a more comprehensive understanding of its use and application.