COVID-19 BÜYÜK SALGIN DALGASININ İSTATİSTİKSEL YAZILIM YOLUYLA MODELLENMESİ: NİCELİKSEL RİSK DEĞERLENDİRMESİ VE TANIMLAMA ANALİZİ

Year 2022, , 145 - 161, 31.01.2022

Mostafa Eissa

https://doi.org/10.35232/estudamhsd.1024129

Cited By: 3

Abstract

COVID-19 küresel salgını, son zamanlarda halk sağlığı kriterlerine dayalı etkili bir tehlike indeksi arayışını teşvik etti. Buradaki çalışma, bir ada ülkesindeki seçilmiş salgın vakaları üzerinden sağlık tehlikesi tahmini ve risk analizi için kantitatif teknikleri tartışmaktadır. Vaka araştırması, vakaların ve ölüm paternlerinin kronolojik şekilde görsel olarak izlenmesine ek olarak tanımlayıcı analiz, kontrol grafikleri, Pareto grafikleri, veri modeleme gibi istatistiksel süreç metodolojilerinin bir kombinasyonunu içerir. Trend grafikleri, salgın atağının iki dalga şeklinde olduğunu gösterdi: ilk büyük ve keskin tepe noktası, ardından başka bir minor relaps meydana gelmeden önce düşük seviyeli bir dalga. Morbidite oranı, hastalığın toplam ülke nüfusunun yaklaşık %0,02'lik katkısıyla düşüktü. Dönüştürülmüş enfeksiyon vakaları sayısının dağılımı, ana dalgada Gauss dağılımını takip ederken ölüm sayısı, normal dağılımdan çarpıklık ve basıklık değerlerinin önemli ölçüde kaymasının gösterilmesiyle verilerin normal yayılım belirtilerini gösteremedi. Bununla birlikte, çalışma süresi boyunca bireysel vaka ve ölüm sayılarının genel dağılımı, sıfırın alt değeriyle sınırlı, kesikli bir dağılım göstermiştir. Büyük dalganın kümülatif vakalar ve ölümler olarak matematiksel açıklaması, Richards modelini iyi bir regresyonla izledi (r>0,996). Yerleşik analiz, salgınların incelenmesi için tıp alanında değerli olabilecek basit, ucuz istatistiksel programlar kullanılarak ölüm/hastalığa dayalı pandemi etkisinin hızlı nicel değerlendirmesi için bir kilometre taşı görevi görmektedir.

Keywords

Kontrol grafikleri, COVID-19, morbidite/mortalite, Yeni Zelanda, Pareto grafiği, Richard modeli

MODELING OF COVID-19 MAJOR OUTBREAK WAVE THROUGH STATISTICAL SOFTWARE: QUANTITATIVE RISK EVALUATION AND DESCRIPTION ANALYSIS

Abstract

The recent COVID-19 global pandemic has stimulated a search for an effective hazard index based on public health criteria. The study herein is discussing quantitative techniques for health hazard estimation and analysis of risk through selected epidemic cases in an island country. The case investigation comprises a combination of unique statistical process methodologies of descriptive analysis, control charts, Pareto charts, data modeling, in addition to the visual monitoring of cases and death patterns chronologically. Trending charts showed that the outbreak attack takes the form of two waves: the first major and sharp peak followed by a low noise level before another minor relapse occurs. The morbidity rate was low with the contribution of illness from the total country population of approximately 0.02%. While the dispersion of the number of evolved cases of infection followed Gaussian distribution in the major wave, the mortality number failed to show signs of normal spreading of data indicated by significant drifting of skewness and kurtosis values from the normal distribution. However, the overall dispersion of the individual counts of cases and deaths during the period of the study demonstrated truncated distribution limited by the lower value of zero. Mathematical description of the major wave as cumulative cases and deaths followed the Richards model with good regression (r>0.996). The established analysis serves as a milestone for swift quantitative assessment of the pandemic impact based on mortality/morbidity using simple inexpensive statistical programs which would be valuable in the medical field for the study of outbreaks.

Keywords

Control chart, COVID-19, morbidity/mortality, New Zealand, Pareto diagram, Richard Model

References

• 1. Rodriguez-Morales AJ, Bonilla-Aldana DK, Balbin-Ramon GJ, Rabaan AA, Sah R, Paniz-Mondolfi A, Pagliano P, et al. History is repeating itself: Probable zoonotic spillover as the cause of the 2019 novel Coronavirus Epidemic. Infez Med. 2020;28(1):3-5.
• 2. Ritchie H, Mathieu E, Rodés-Guirao L, Appel C, Giattino C, Ortiz-Ospina E et al. Coronavirus Pandemic (COVID-19) (Internet). Our World in Data. 2021 (cited 14 October 2021). Available from: https://ourworldindata.org/coronavirus
• 3. Breur T. Statistical Power Analysis and the contemporary “crisis” in social sciences. Journal of Marketing Analytics. 2016;4(2-3):61-5.
• 4. Cox D, Kartsonaki C, Keogh R. Big data: Some statistical issues. Statistics & Probability Letters. 2018;136:111-5.
• 5. Inferring From Data (Internet). Home.ubalt.edu. 2021 (cited 14 October 2021). Available from: http://home.ubalt.edu/ntsbarsh/business-stat/stat-data/topics.htm
• 6. Holzinger A. Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Informatics. 2016;3(2):119-31.
• 7. Wooley J, Lin H. Catalyzing Inquiry at the Interface of Computing and Biology. Washington: National Academies Press. 2006.
• 8. Eissa MEAM. Global Health Quality Assessment Using Statistical Control Monitoring Tools Based on Who Database Record: A Descriptive Analysis. Health Research. 2019;3:8-18.
• 9. Rashed E, Eissa M. Long-Term Quantitative Assessment of Women Survivability from Cancer: A Unique Descriptive Analysis. Highlights in BioScience. 2020.
• 10. Data.europa.eu (Internet). Data.europa.eu. 2021 (cited 21 January 2021). Available from: https://data.europa.eu/euodp/en/home
• 11. The New Zealand Oxford Dictionary. 2005 [cited 2021 Jan 30]. Available from: https://doi.org/10.1093/acref/9780195584516.001.0001
• 12. Wilmshurst J, Hunt T, Lipo C, Anderson A. High-precision radiocarbon dating shows recent and rapid initial human colonization of East Polynesia. Proceedings of the National Academy of Sciences. 2010;108(5):1815-20.
• 13. Chepkemoi J. Which Are The Island Countries Of The World? (Internet). WorldAtlas. 2021 (cited 14 October 2021). Available from: https://web.archive.org/web/20171207094959/http://www.worldatlas.com/articles/which-are-the-island-countries-of-the-world.html
• 14. Evans M, McCabe G, Moore D. Minitab manual for Moore and McCabe's Introduction to the practice of statistics, third edition. New York: W.H. Freeman; 1999.
• 15. Overview for Contour Plot - Minitab (Internet). Support.minitab.com. 2021 (cited 14 October 2021). Available from: https://support.minitab.com/en-us/minitab/18/help-and-how-to/modeling-statistics/u sing-fitted-models/how-to/contour-plot/before-you-start/overview/#:~:text=Learn%2 0more%20about%20Minitab%2018,connected%20to%20produce%20contour%20lines
• 16. MINITAB user's guide 2. State College, PA: Minitab Inc.; 2000.
• 17. Slager D, Slager A. Essential Excel 2019. NY, USA: Apress L. P.; 2020.
• 18. All statistics and graphs for Laney U' Chart - Minitab (Internet). Support.minitab.com. 2021 (cited 14 October 2021). Available from: https://support.minitab.com/en-us/minitab/19/help-and-how-to/quality-and-processimprovement/control-charts/how-to/attributes-charts/laney-u-chart/interpret-the-results/all-statistics-and-graphs/
• 19. Glushkovsky E. ‘On-line’G-control chart for attribute data. Quality and Reliability Engineering International. 1994;10(3):217-227.
• 20. GraphPad Software L. GraphPad Prism 9 Statistics Guide - How to: Descriptive statistics (Internet). Graphpad.com. 2021 (cited 14 October 2021). Available from: https://www.graphpad.com/guides/prism/latest/statistics/stat_howto_columnstatistics.htm
• 21. Kung S, Doppen M, Black M, Hills T, Kearns N. Reduced mortality in New Zealand during the COVID-19 pandemic. The Lancet. 2021;397(10268):25.
• 22. Coughlan C. NZ is 'past the peak', but Covid-19 deaths are a sobering reminder to stay the course (Internet). Stuff. 2021 [cited 2021 Oct 14]. Available from: https://www.stuff.co.nz/national/health/coronavirus/121001821/coronavirus-all-of-government-covid19-national-response-update
• 23. Strongman S. Covid-19 pandemic timeline (Internet). Shorthand.radionz.co.nz. 2021 [cited 2021 Oct 14]. Available from: https://shorthand.radionz.co.nz/coronavirus-timeline/
• 24. Eissa ME, Mahmoud AM, Nouby AS. Active versus Passive Microbiological Air Sampling Risk Assessment: Relation and Comparative Study in Pharmaceutical Industry. Research & Reviews: A Journal of Pharmaceutical Science. 2016;7(1):13-27.
• 25. New Zealand confirms 12 new cases of COVID-19 amid second wave - Xinhua | English.news.cn [Internet]. Xinhuanet.com. 2021 [cited 2021 Oct 14]. Available from: http://www.xinhuanet.com/english/2020-08/14/c_139290059.htm
• 26. Mohammed M, Laney D. Overdispersion in health care performance data: Laney's approach. Quality and Safety in Health Care. 2006;15(5):383-4.
• 27. Xie M, Goh T, Kuralmani V. Statistical Models and Control Charts for High-Quality Processes. Boston, MA: Springer US; 2002.
• 28. Jefferies S, French N, Gilkison C, Graham G, Hope V, Marshall J et al. COVID-19 in New Zealand and the impact of the national response: a descriptive epidemiological study. The Lancet Public Health. 2020;5(11):e612-e623.
• 29. Hsieh Y, Fisman D, Wu J. On epidemic modeling in real time: An application to the 2009 Novel A (H1N1) influenza outbreak in Canada. BMC Research Notes. 2010;3(1).
• 30. Wang X, Wu J, Yang Y. Richards model revisited: Validation by and application to infection dynamics. Journal of Theoretical Biology. 2012;313:12-9.