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Koronavirüsün Yayılmasının Tahmini Model Simülasyonu ve Taguchi Analizi

Year 2022, Volume: 9 Issue: 2, 814 - 828, 31.05.2022
https://doi.org/10.31202/ecjse.1012718

Abstract

Yapılan simülasyon çalışmasında Çin’deki verilere göre 8 farklı senaryo analizleri yapılmıştır. Öncelikle salgının başladığı andan itibaren tedbirlerin ilk alınma süresinin pandemi sürecine olan etkisi incelenmiştir. En kısa sürede önlemlerin alınmasının pandemi sürecini azalttığı görülmüştür. Daha sonra nüfus sayılarının pandemi üzerindeki etkisinin simülasyon analizinde ise daha küçük gruplarda kontrolün kolay olduğu ve pandemi sürecinin 100000 nüfuslu bir yerde 180 günde sonlandırılabileceği görülmüştür. Ayrıca farklı senaryo analizlerine ek olarak pandemi sürecine etki eden parametrelerin (aktarım hızı, popülasyon sayısı ve önlemlerin alındığı sürenin) etkisi Taguchi analizi ile incelenmiştir. Aktarım hızının salgın sürecinde en etkili (%35) parametre olduğu görülmüştür. Ancak aktarım hızı yani ilk enfeksiyonlu kişilerin diğer insanlar ile temas kontrolünün önlemlerin alınmaya başlayacağı sürede kontrol edilmesi mümkün olamayacağı için nüfus ve önlemlerin alınma süresi üzerinde durulması gerekmektedir. Analiz sonuçlarına göre optimum koşulların aktarım hızı 0.2, önlemlerin alınma süresi 10. gün ve nüfus sayısı 100000 olarak elde edilmiştir. Bu optimum koşulda ise 90 günde pandemi sürecinin sonlandığı görülmüştür. Yapılan simülasyon sonucuna göre salgında nüfusu küçük gruplara bölerek en kısa sürede önlemlerin alınması gerekmektedir. Ayrıca model doğrulaması için yapılan simülasyon sonucunun gerçek veriler ile karşılaştırılarak sonuçların birbirine yakın olarak değiştiği görülmüştür.

References

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  • Oztop, M.H, Sahin, S., Sumnu, G., Optimization of Microwave Frying of Potato Slices by using Taguchi Technique, Journal of Food Engineering, 2007, 79(1): 83-91.
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Forecasting Modeling Simulation and Taguchi Analysis of The Dissemination of Covid 19

Year 2022, Volume: 9 Issue: 2, 814 - 828, 31.05.2022
https://doi.org/10.31202/ecjse.1012718

Abstract

The simulation study conducted 8 different scenario analyses based on data in China. Primarily from the moment the outbreak began, the impact of the first time the measures were taken on the pandemic process was examined. Taking measures as soon as possible appeared to reduce the pandemic process. Simulation analysis of the effect of population numbers on the pandemic later found that control was easy in smaller groups and that the pandemic process could be terminated in 180 days in a 100000 populated location. In addition to different scenario analyses, the impact of parameters (transmission rate, taking measures and population number) that act on the pandemic process was examined with the Taguchi analysis. The transfer rate was found to be the most effective (35%) parameter in the outbreak process. However, there is a need to focus on the population and the length of time it takes for people with initial infections to have contact control with other people to be checked as soon as measures start to take place. According to the results of the analysis, the transmission rate of the optimum conditions is 0.2, the taking measures are taken is 10th day and population number is 100000. In this optimal condition, the pandemic process was terminated in 90 days. According to the simulation results, measures should be taken as soon as possible, dividing the population into small groups. Furthermore, the simulation result for model validation was compared to actual data, showing that the results varied closely together.

References

  • Camacho, A., Kucharski, A., Aki-Sawyerr, Y., White, M.A., Flasche, S., Baguelin, M., Pollington, T., Carney, J.R., Glover, R., Smout, E., Tiffany, A., Edmunds, W.J., Funk, S., Temporal Changes in Ebola Transmission in Sierra Leone and Implications for Control Requirements: a Real-time Modelling Study, PLoS Current Outbreaks, Edition 1, Feb 10, 2015.
  • Dash, S., Chakravarty, S., Mohanty, S.N., Pattanaik, C.R., Jain, S., A Deep Learning Method to Forecast COVID-19 Outbreak, New Generation Computing, 2021, Jul 18:1-25.
  • Mohammad Masum, A.K., Khushbu, S.A., Keya, M., Abujar, S., Hossain, S.A., COVID-19 in Bangladesh: A Deeper Outlook into The Forecast with Prediction of Upcoming Per Day Cases Using Time Series, Procedia Computer Science, 2020, 178:291-300.
  • Rahimi, I., Chen, F., Gandomi, A.H., A review on COVID-19 forecasting models, Neural Computing & Applications, 2021, Feb 4:1-11.
  • Shinde, G.R., Kalamkar, A.B., Mahalle, P.N., Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art, SN Computer Science, 2020, 1, 197.
  • Naude, W., Artificial intelligence against COVID-19: an early review, IZA Discussion Paper No. 13110, 2020.
  • Keeling, M.J., Eames, K.T.D., Networks and epidemic models, J. R. Soc. Interface, 2005, 2, 295–307.
  • He, S., Peng, Y., Sun, K., SEIR modeling of the COVID-19 and its dynamics, Nonlinear Dyn ,2020, 101, 1667–1680.
  • Tang, B., Wang, X., Li, Q., Bragazzi, N. L., Tang, S., Xiao, Y., Wu, J., Estimation of the transmission risk of the 2019-ncov and its implication for public health interventions, J. Clin. Med., 2020, 9, 462.
  • Fontes, E., Modeling the Spread of COVID-19 with COMSOL Multiphysics, Comsol Blogs, https://www.comsol.com/blogs/modeling-the-spread-of-covid-19-with-comsol-multiphysics/, 2020.
  • Weiss, H., The SIR model and the Foundations of Public Health, Materials Matematics, 2013, no. 3, pp. 1–17.
  • Höhle, M., Flatten the COVID-19 curve, Theory meets practice, https://staff.math.su.se/hoehle/blog/2020/03/16/flatteningthecurve.html, 2020.
  • Verity, R., Okell, L.C., Dorigatti, I., Winskill, P., Whittakeri, C., Estimates of the severity of coronavirus disease 2019: a model-based analysis, The Lancet Infectious Diseases, 2020.
  • Kucharski, A.J., Russell, T.W., Diamond, C., Liu, Y., Edmunds, J., Funk, S., Eggo, R.M., Early dynamics of transmission and control of COVID-19: a mathematical modelling study, The Lancet Infectious Diseases, 2020.
  • Oztop, M.H, Sahin, S., Sumnu, G., Optimization of Microwave Frying of Potato Slices by using Taguchi Technique, Journal of Food Engineering, 2007, 79(1): 83-91.
  • Yang, W.H., Tarng Y.S., Design optimization of cutting parameters for turning operations based on the Taguchi method, Journal of Materials Processing Technology, 1998, 84(1-3): 122-129.
  • Taguchi, G., Introduction to quality engineering, Asian Productivity Organization, Tokyo, 1990.
There are 17 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Makaleler
Authors

Burak Türkan 0000-0002-4019-7835

Hüsniye Merve Bingöl Türkan This is me 0000-0001-9849-056X

Publication Date May 31, 2022
Submission Date October 20, 2021
Acceptance Date January 6, 2022
Published in Issue Year 2022 Volume: 9 Issue: 2

Cite

IEEE B. Türkan and H. M. Bingöl Türkan, “Forecasting Modeling Simulation and Taguchi Analysis of The Dissemination of Covid 19”, El-Cezeri Journal of Science and Engineering, vol. 9, no. 2, pp. 814–828, 2022, doi: 10.31202/ecjse.1012718.
Creative Commons License El-Cezeri is licensed to the public under a Creative Commons Attribution 4.0 license.
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