A NONLINEAR REGRESSION MODEL, ANALYSIS AND SIMULATIONS FOR THE SECOND WAVE OF COVID-19: THE CASE STUDY OF TURKEY

Year 2021, Volume: 22 Issue: 1, 36 - 44, 26.03.2021

Sıla Övgü Korkut Uysal , Nurcan Gücüyenen Yeşim Çiçek

https://doi.org/10.18038/estubtda.801006

Cited By: 1

Abstract

COVID-19 pandemic disease gained major attention among scientists due to its high mortality/ infectiousness rate. Moreover, the analysis of this disease requires much attention by the Government to take precautions and construct strategies. This study aims to develop a new nonlinear model for COVID-19. The main focus is the time when the number of daily infected individuals has begun to increase constantly. To this end, the time series from 1 August 2020 to 22 September 2020 is conducted. Moreover, the proposed model takes into account the disease characteristics. After the model parameters are obtained by detailed mathematical analysis by the trained data, the model is validated by the test/evaluation data set. The results and simulations show that the proposed model has a perfect match with the raw data. Furthermore, the calculated standard errors when compared by the population of Turkey are evidence of how well the model fits the raw data. This study is important not only because it achieves good results but also because it is the first nonlinear regression model including its mathematical analysis for the COVID-19 pandemic.

Keywords

COVID-19,, Nonlinear Regression,, Nonlinear Analysis, Simulation, Standard Error

References

• [1]Atangana, A. (2020). Modeling the spread of COVID-19 with new fractal-fractional operators. Can the lockdown save mankind before vaccination? Chaos, Solitons, Fractals, 136, 109860.
• [2]Atangana, A., & Igret-Araz, S. (2020). Mathematical model of COVID-19 spread in Turkey and South Africa: Theory, methods, and applications. MedRxiv. DOI: https://doi.org/10.1101/2020.05.08.20095588
• [3]Cooper, I., Mondal, A., & Antonopoulos, C. G. (2020). A SIR model assumption for the spread of COVID-19 in different communities. Chaos, Solitons, and Fractals, 139, 110057.
• [4]Djilalia, S., & Ghanbari, B. (2020 ). Coronavirus pandemic: A predictive analysis of the peak outbreak epidemic in South Africa, Turkey, and Brazil. Chaos Solitons Fractals., 138, 109971.
• [5]Fanelli, D., & Piazza, F. (2020). have analyzed and forecasted the trend of COVID-19 spreading in China, Italy, and France. Chaos Solitons Fractals, 134, 109761.
• [6]Ghanbari, B. (2020). On forecasting the spread of the COVID-19 in Iran: The second wave. Chaos, Solitons, and Fractals, 140, 110176.
• [7]Ivorra, B., Ferrandez, M., Vela-Perez, M., & Ramos, A. (2020). Mathematical Modelling of the spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected infections. The case of China. Commun. Nonlinear Sci. Numer. Simulations, 88, 105303.
• [8]Ivorra, B., Ngom, D., & Ramos, A. M. (2015). Be-CoDiS: A Mathematical Model to Predict the Risk of Human Diseases Spread Between Countries-Validation and Application to the 2014-2015 Ebola Virus Disease Epidemic. Bull Math Biol, 77(9), 1668-1704.
• [9]Ministry, R. T. Rebuplic of Turkey Ministry, COVID-19 Information Page. https://covid19.saglik.gov.tr/TR-66122/genel-koronavirus-tablosu.html
• [10]World Health Organization (2019). Coronavirus disease (COVID-19) outbreak. https://www.who.int/emergencies/diseases/novel-coronavirus-2019 [11]Ozdinc, M., Senel, K., Ozturkcan, S., & Akgul, A. (2020). Predicting the Progress of COVID-19: The Case for Turkey. Turkiye Klinikleri Journal of Medical Sciences, 40(2), 117-119.
• [12]Pandey, G., Chaudhary, P., Gupta, R., & Pal, S. (2020). SEIR and Regression Model-based COVID-19 outbreak predictions in India. Medrxiv. DOI: https://doi.org/10.1101/2020.04.01.20049825
• [13]Velásquez, R., & Vanessa, J. (2020). Forecast and evaluation of COVID-19 spreading in the USA with reduced-space Gaussian process regression. Chaos Solitons Fractals, 136, 109924.
• [14]World Health Organization, 2. (2020). WHO Coronavirus Disease (COVID-19) Dashboard. https://covid19.who.int/