DSÖ Avrupa Bölgesinde grip salgınının büyüklüğünün tahmin edilmesi

Yıl 2025, Cilt: 14 Sayı: 1, 64 - 73, 23.03.2025

Tevfik Bulut

Öz

Bu çalışma DSÖ Avrupa ülkeleri ve bölgelerindeki grip salgınının büyüklüğünü tahmin etmeyi amaçlamaktadır. Bu ülkelerin dalga boyları, A ve B alt tipleri de dahil olmak üzere influenza vakalarının sayısına göre karşılaştırılmıştır. Salgının büyüklüğünü değerlendirmek için nüfus yoğunluğu, insani gelişme endeksi, vaka sayısı ve ilk vakanın kaydedilmesinden bu yana geçen gün sayısı gibi faktörler göz önünde bulundurularak epidemiyolojik dalga boyu yöntemi kullanılmıştır. Birleşik Krallık, İngiltere, 2022, 2023 ve 2024 yıllarında en yüksek dalga boyu skorlarına (We) sahip olmuştur. Buna karşılık, Azerbaycan sırasıyla 2022 ve 2023 yıllarında en düşük dalga boyu skorlarına (We) sahip olmuştur. DSÖ Avrupa ülkeleri ve bölgeleri için ortalama dalga boyu skoru 2023'te zirveye ulaşırken, en düşük skor 13,44 We ile 2022'de görülmüştür. Çalışma, epidemiyolojik dalga boyu yönteminin salgın boyutunu tahmin etmek için kullanılabileceğini ve salgının daha net ve daha güvenilir bir kesitsel görüntüsünü sağlayabileceğini göstermektedir.

Anahtar Kelimeler

Grip, DSÖ Avrupa Bölgesi, Salgın, Epidemiyolojik Dalga Boyu Yöntemi

Estimation of the size of influenza epidemic in the WHO European Region

Öz

This study aims to estimate the magnitude of the influenza epidemic in WHO European countries and territories. The wavelengths of these countries were compared based on the number of influenza cases, including subtypes A and B. The epidemiological wavelength method was used to assess the outbreak's magnitude, considering factors like population density, human development index, case count, and the number of days since the first case was recorded. The UK, England, had the highest wavelength scores (We) in 2022, 2023, and 2024. Conversely, Azerbaijan had the lowest wavelength scores (We) in 2022 and 2023, respectively. The average wavelength score for WHO European countries and territories reached its peak in 2023, with the lowest score in 2022 at 13.44 We. The study suggests that the epidemiological wavelength method can be used to estimate outbreak size, providing a clearer and more reliable cross-sectional image of the epidemic.

Anahtar Kelimeler

Influenza, WHO European Region, Epidemic, Epidemiological Wavelength Method

Etik Beyan

Ethical approval has not been obtained as the data are public.

Kaynakça

• 1. WHO. (2023). “Influenza (Seasonal)”. https://www.who.int/news-room/fact-sheets/detail/influenza-(seasonal) (Access Date: 02.11.2024).
• 2. WHO. (2009). “Influenza”. https://www.who.int/europe/news-room/fact-sheets/item/influenza (Access Date: 02.11.2024.).
• 3. WHO. (2010). “Seasonal influenza”. https://www.who.int/europe/news-room/fact-sheets/item/seasonal-influenza (Access Date: 02.11.2024).
• 4. “Influenza virus characterization: summary report, Europe”. (2024). Copenhagen: WHO Regional Office for Europe and Stockholm: European Centre for Disease Prevention and Control; 2024
• 5. Bulut, T., & Top, M. (2023). “Estimation of the size of the COVID-19 pandemic using the epidemiological wavelength model: results from OECD countries”. Public Health, 220, 172-178.
• 6. World Health Organization (WHO). (2024). Global Influenza Program, FluNet database, https://www.who.int/tools/flunet (Access Date: 28.10.2024).
• 7. Hannah Ritchie and Edouard Mathieu (2019). “Which countries are most densely populated?”. https://ourworldindata.org/most-densely-populated-countries (Access Date:28.10.2024).
• 8. Office for National Statistics (ONS), released 26 March 2024, ONS website, statistical bulletin, “Population estimates for the UK, England, Wales, Scotland, and Northern Ireland: mid-2022”. https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/bulletins/annualmidyearpopulationestimates/mid2022#cite-this-statistical-bulletin (Access Date:28.10.2024).
• 9. UN. “Human Development Index (HDI)”. https://hdr.undp.org/data-center/human-development-index#/indicies/HDI (Access Date: 28.10.2024).
• 10. Microsoft Corporation. “Microsoft Office Excel”. 2018.
• 11. R Core Team. 2022. “R: A language and environment for statistical computing”. R Foundation for Statistical Computing, Vienna, Austria.
• 12. UNDP (United Nations Development Programme). 2024. “Human Development Report 2023-24: Breaking the gridlock: Reimagining cooperation in a polarized world”. New York.
• 13. Pélabon, C., Hilde, C. H., Einum, S., & Gamelon, M. (2020). “On the use of the coefficient of variation to quantify and compare trait variation”. Evolution Letters, 4(3), 180–188. https://doi.org/10.1002/evl3.171
• 14. Vicente, P., & Suleman, A. (2022). “COVID-19 in Europe: from outbreak to vaccination”. BMC Public Health, 22(1). https://doi.org/10.1186/s12889-022-14454-5
• 15. Fong, M. W., Gao, H., Wong, J. Y., Xiao, J., Shiu, E. Y., Ryu, S., & Cowling, B. J. (2020). “Nonpharmaceutical Measures for Pandemic Influenza in Nonhealthcare Settings—Social Distancing Measures”. Emerging Infectious Diseases, 26(5), 976–984. https://doi.org/10.3201/eid2605.190995
• 16. Adlhoch, C., Sneiderman, M., Martinuka, O., Melidou, A., Bundle, N., Fielding, J., Olsen, S. J., Penttinen, P., Pastore, L., & Pebody, R. (2021). “Spotlight influenza: The 2019/20 influenza season and the impact of COVID-19 on influenza surveillance in the WHO European Region”. Eurosurveillance, 26(40). https://doi.org/10.2807/1560-7917.es.2021.26.40.2100077
• 17. Iderus, N. H. M., Singh, S. S. L., Ghazali, S. M., Ling, C. Y., Vei, T. C., Zamri, A. S. S. M., Jaafar, N. A., Ruslan, Q., Jaghfar, N. H. A., & Gill, B. S. (2022). “Correlation between Population Density and COVID-19 Cases during the Third Wave in Malaysia: Effect of the Delta Variant”. International Journal of Environmental Research and Public Health, 19(12), 7439. https://doi.org/10.3390/ijerph19127439
• 18. Jamal, Y., Gangwar, M., Usmani, M., Adams, A. E., Wu, C., Nguyen, T. H., Colwell, R., & Jutla, A. (2022). “Identification of Thresholds on Population Density for Understanding Transmission of COVID‐19”. GeoHealth, 6(9). https://doi.org/10.1029/2021gh000449
• 19. Ito, G., Takazono, T., Hosogaya, N., Iwanaga, N., Miyazawa, S., Fujita, S., Watanabe, H., & Mukae, H. (2023). “Impact of meteorological and demographic factors on the influenza epidemic in Japan: a large observational database study”. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-39617-1
• 20. Wong, D. W. S., & Li, Y. (2020). “Spreading of COVID-19: Density matters”. PLoS ONE, 15(12), e0242398. https://doi.org/10.1371/journal.pone.0242398
• 21. Martins-Filho, P. R. (2021). “Relationship between population density and COVID-19 incidence and mortality estimates: A county-level analysis”. Journal of Infection and Public Health, 14(8), 1087–1088. https://doi.org/10.1016/j.jiph.2021.06.018
• 22. Gibbons, C. L., Mangen, M. J., Plass, D., Havelaar, A. H., Brooke, R. J., Kramarz, P., Peterson, K. L., Stuurman, A. L., Cassini, A., Fèvre, E. M., & Kretzschmar, M. E. (2014). “Measuring underreporting and under-ascertainment in infectious disease datasets: a comparison of methods”. BMC Public Health, 14(1). https://doi.org/10.1186/1471-2458-14-147
• 23. Boonwaat, L., Fletcher-Lartey, S., & Conaty, S. (2016). “Underreporting of influenza outbreaks in aged care facilities in South Western Sydney, Australia, 2014”. Western Pacific Surveillance Response Journal, 7(1), 32–34. https://doi.org/10.5365/wpsar.2015.6.3.001
• 24. Chong, K. C., Fong, H. F., & Zee, C. Y. (2013). “Estimating the incidence reporting rates of new influenza pandemics at an early stage using travel data from the source country”. Epidemiology and Infection, 142(5), 955–963. https://doi.org/10.1017/s0950268813002550