Modelling and Prediction of Covid-19 Epidemic in Turkey Comparing with USA and China

Year 2021, , 111 - 126, 31.08.2021

Engin Ergül , Halil İbrahim Kurt , Murat Oduncuoğlu , Necip Fazıl Yılmaz

https://doi.org/10.30931/jetas.790465

Abstract

The aim of the study is to research and compare the influences of the confirmed cases, test number and time range on the death and recovery rates in the United State of America, China, and Turkey, and to find out the effect of the epidemic in the near future of Turkey. The modelling and prediction of effects of the day, case and test numbers of COVID-19 infection in the USA, China and Turkey are carried out using the artificial neural network approach (ANN). The system are trained and tested with the different numbers of neurons, hidden layers and activation functions to increase the reliability and accuracy of model. The proposed models have a high R2 value for China and Turkey. We can say according to the results that the measures taken by the USA are inadequate. The formulation is applied to predict the effect of Covid-19 infection in Turkey. The test number that is an important factor in detecting the cases should be increased. The results show a good fit between the observed data and those obtained by the ANN model. If the precautions are strictly followed, the case number will be decreased significantly after 160 days for Turkey according to result of the proposed model but due to the uncontrolled variables, this time may result in between 200 and 250 days.

Keywords

Covid-19, Modelling, Prediction, USA, China, Turkey

References

• [1] Cooper, I., Mondal, A., Antonopoulos, C. G., “A SIR model assumption for the spread of COVID-19 in different communities”, Chaos, Solitons & Fractals, (2020) : 110057.
• [2] Tezer, H., Demirdağ, T. B., “Novel coronavirus disease (COVID-19) in children”, Turkish Journal of Medical Sciences 50(SI-1) (2020) : 592-603.
• [3] Leal-Neto, O. B., et al., “Prioritizing COVID-19 tests based on participatory surveillance and spatial scanning”, International Journal of Medical Informatics 143 (2020) : 104263.
• [4] Guan, W.-j., et al., “Clinical characteristics of coronavirus disease 2019 in China”, New England Journal of Medicine 382 (2020) : 1708-1720.
• [5] Celik, I., Saatci, E., Eyüboğlu, A. F., “Emerging and reemerging respiratory viral infections up to Covid-19”, Turkish Journal of Medical Sciences 50(SI-1) (2020) : 557-562.
• [6] Yang, X., et al., “Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: A single-centered, retrospective, observational study”, The Lancet Respiratory Medicine, 8(5) (2020) : 475-481.
• [7] Kurt, H., Oduncuoglu, M., “Formulation of the effect of different alloying elements on the tensile strength of the in situ Al-Mg2Si composites”, Metals 5(1) (2015) : 371.
• [8] Kurt, H., Oduncuoglu, M., Kurt, M., “A mathematical formulation to estimate the effect of grain refiners on the ultimate tensile strength of Al-Zn-Mg-Cu alloys”, Metals 5(2) (2015) : 836.
• [9] Togun, N., et al., “Formulation of effects of atropine, pralidoxime and magnesium sulfate on cardiac tissue levels of nitric oxide, malondialdehyde and glutathione in organophosphate poisoning using artificial neural network”, Computers in Biology and Medicine 40(1) (2010) : 29-36.
• [10] Ardakani, A. A., et al., “Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks”, Computers in Biology and Medicine 121 (2020) : 103795.
• [11] Ankarali, H., et al., “A statistical modeling of the course of COVID-19 (SARS-CoV-2) outbreak: A comparative analysis”, Asia Pacific Journal of Public Health 32(4) (2020) : 155-160.
• [12] Ghosh, S., “Predictive model with analysis of the initial spread of COVID-19 in India”, International Journal of Medical Informatics 143 (2020) : 104262.
• [13] Mollalo, A., et al., “Predicting the hotspots of age-adjusted mortality rates of lower respiratory infection across the continental United States: Integration of GIS, spatial statistics and machine learning algorithms”, International Journal of Medical Informatics 142 (2020) : 104248.
• [14] Wollenstein-Betech, S., Cassandras, C. G., Paschalidis, I. C., “Personalized Predictive Models for Symptomatic COVID-19 Patients Using Basic Preconditions: Hospitalizations, Mortality, and the Need for an ICU or Ventilator”, International Journal of Medical Informatics 142 (2020) : 104258.
• [15] Eroglu, E., Bozkurt, E., Esenpinar, A. A., Tek, S., “Mathematical analysis of Covid-19 Phenomenon”, Journal of Engineering Technology and Applied Sciences 5(2) (2020) : 59-64.
• [16] Kurt, H. I., Oduncuoglu, M., “Effects of temperature, time, magnesium, and copper on the wettability of Al/TiC system”, Mathematical Problems in Engineering 710526 (2015) : 6.
• [17] Kurt, H. I., Oduncuoglu, M., “Application of a neural network model for prediction of wear properties of ultrahigh molecular weight polyethylene composites”, International Journal of Polymer Science 315710 (2015).
• [18] Shi, P., et al., “Impact of temperature on the dynamics of the COVID-19 outbreak in China” Science of The Total Environment 728 (2020) : 138890.
• [19] Aldaco, R., et al., “Environmental and nutritional impacts of dietary changes in Spain during the COVID-19 lockdown”, Science of The Total Environment 748 (2020) : 140524.
• [20] Whittemore, P. B., “COVID-19 fatalities, latitude, sunlight, and vitamin D” American Journal of Infection Control 48(9) (2020) : 1042-1044.
• [21] Berman, J. D., Ebisu, K., “Changes in U.S. air pollution during the COVID-19 pandemic”, Science of The Total Environment 739 (2020) : 139864.
• [22] Abid, K., et al., “Progress of COVID-19 epidemic in Pakistan”, Asia Pacific Journal of Public Health 32(4) (2020) : 154-156.
• [23] Tufan, A., Güler, A. A., Matucci-Cerinic, M., “COVID-19, immune system response, hyperinflammation and repurposing antirheumatic drugs”, Turkish Journal of Medical Sciences 50(SI-1) (2020) : 620-632.