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

Sıla Övgü KORKUT UYSAL; Nurcan GÜCÜYENEN; Yeşim ÇİÇEK

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

Makale detayları

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

Yazarlar:

Sıla Övgü KORKUT UYSAL | Nurcan GÜCÜYENEN | Yeşim ÇİÇEK

Yayıncı:

Eskisehir Technical University

Ana Başlıklar:

Astronomi ve ilgili bilimler

Dergi:

Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 2021 Vol.22 Issue 1, pp.36-44

Belge Tipi:

Makale

Yayın Tarihi:

3/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000416867

pISSN:

2667-4211

DOI:

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

Özet:

 

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.

 


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.

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Makale detayları

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

Yazarlar: Sıla Övgü KORKUT UYSAL | Nurcan GÜCÜYENEN | Yeşim ÇİÇEK

Yayıncı: Eskisehir Technical University

Ana Başlıklar: Astronomi ve ilgili bilimler

Dergi: Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 2021 Vol.22 Issue 1, pp.36-44

Belge Tipi: Makale

Yayın Tarihi: 3/2021

Belge Dili: İngilizce

Document ID (Org No.): 2000416867

pISSN: 2667-4211

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

Sayı

Yazarlar:

Sıla Övgü KORKUT UYSAL | Nurcan GÜCÜYENEN | Yeşim ÇİÇEK

Yayıncı:

Eskisehir Technical University

Ana Başlıklar:

Astronomi ve ilgili bilimler

Dergi:

Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 2021 Vol.22 Issue 1, pp.36-44

Belge Tipi:

Makale

Yayın Tarihi:

3/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000416867

pISSN:

2667-4211

DOI:

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