|تعداد مشاهده مقاله||7,656,831|
|تعداد دریافت فایل اصل مقاله||5,868,809|
Regression and validation studies of the spread of novel COVID-19 in Iraq using mathematical and dynamic neural networks models: A case of the first six months of 2020
|Caspian Journal of Environmental Sciences|
|دوره 19، شماره 3، مهر 2021، صفحه 431-440 اصل مقاله (1.28 M)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22124/cjes.2021.4930|
|Anees A. Khadom* 1؛ A Khudhair Al-Jiboory2؛ Mustafa S. Mahdi1؛ Hameed B. Mahood3|
|1Department of Chemical Engineering, College of Engineering – University of Diyala – Baquba City 32001, Diyala governorate, Iraq|
|2Department of Mechanical Engineering, College of Engineering – University of Diyala – Baquba City 32001, Diyala governorate, Iraq|
|3Department of Chemical and Process Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK|
|The dramatic spread of COVID-19 has put the entire world at risk. In this work, the spread of COVID-19 in Iraq has been studied. Due to the increase in the number of positive cases and deaths with this disease, huge pressure acts on the economy and world professionals worldwide. Therefore, building mathematical models to predict the growth of this serious disease is extremely useful. It helps to predict the future numbers of cases in Iraq. Therefore, dynamic neural networks and curve fitting techniques have been developed to construct such a model. Data from the World Health Organization (WHO) are used as a source for mathematical model construction. The period between 25, February to 18, June 2020 was used for regression, validation, and model construction of Dynamic Neural Networks (DNN). Nine samples (19 – 27 June 2020) were used for predicting the future infected and death cases. Descriptive statistical studies showed that the standard deviation varies sharply on June as compared with earlier months of 2020. Three mathematical models are proposed. Linear, polynomials (2nd, 3rd, and 4th orders), and exponential models are used to correlate confirmed infected cases (CIC) and confirmed death cases (CDC) that represent the dependent variables as function of time (independent variable). Nonlinear regression based on least-square method is used to estimate the coefficients of models. Exponential models were the most significant with 0.9964 and 0.9974 correlation coefficients for CIC and CDC, respectively. Validation analysis showed a significant deviation between real and predicted cases using exponential models. However, DNN models showed better response than exponential models. This is evidenced by objective and subjective comparisons. Finally, the CIC and CDC may be increased with time to approach 50000 and 2000 respectively at the end of June 2020.|
|Mathematical modelling؛ COVID-19؛ Statistical analysis؛ Confirmed cases؛ Neural networks|
Acter, T 2020, Evolution of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) as coronavirus disease 2019 (COVID-19) pandemic: A global health emergency. Science of the Total Environment, 730: 138996.
Ahmad A, Azid I, Yusof, A & Seetharamu, K 2004, Emission control in palm oil mills using artificial neural network and genetic algorithm. Computers & Chemical Engineering, 28: 2709-2715.
Bendaif H, B. Hammouti, I. Stiane, Y. Bendaif, M.A. El Ouadi, El Ouadi, Y 2020, Investigation of spread of novel coronavirus (COVID-19) pandemic in MOROCCO & estimated confinement duration to overcome the danger phase, Caspian Journal of Environmental Sciences, 18: 149-156.
Connor, J, Martin R & Atlas, L 1994, Recurrent neural networks and robust time series prediction, IEEE Transactions on Neural Networks, 5: 240-254.
Cortegiani, A 2020, A systematic review on the efficacy and safety of chloroquine for the treatment of COVID-19, Journal of Critical Care, 57: 279-283.
Dua, V 2011, An Artificial Neural Network approximation based decomposition approach for parameter estimation of system of ordinary differential equations. Computers & Chemical Engineering, 35: 545-553.
Fahmi I, Cremaschi, S 2012, Process synthesis of biodiesel production plant using artificial neural networks as the surrogate models. Comput. Chem. Eng., 46: 105-123.
Hassan, K, Khadom, A, Kurshed, N 2016, Experimental and Mathematical Studies for Corrosion Reaction of Mild Steel – Sulphuric Acid – Friendly Inhibitor System. European Journal of Scientific Research, 139: 163-170.
Hongyan R, Lu Zhao , An Zhang, Liuyi Song , Yilan Liao, Weili Lu , Cheng, C 2020, Early forecasting of the potential risk zones of COVID-19 in China's megacities, Science of the Total Environment, 729: 138995.
Ibrahim A, Abayomi Toyin Onanuga, Olanrewaju Olugbenga Akinola, Olakitan, W 2020, Modelling spatial variations of coronavirus disease (COVID-19) in Africa, Science of the Total Environment, 729: 138998.
Jiang W, Schotten, H 2020, Deep Learning for Fading Channel Prediction, IEEE Open Journal of the Communications Society, 1: 320-332.
Khadom, A, Aprael S Yaro, AS Altaie, AAH, Kadhum 2009, Mathematical modelling of corrosion inhibition behavior of low carbon steel in HCl acid, Journal of Applied Sciences, 9:2457-2462.
Khadom, A, Yaro, A 2014, Mass transfer effect on corrosion inhibition process of copper–nickel alloy in hydrochloric acid by Benzotriazole, Journal of Saudi Chemical Society, 18: 214-219.
Khadom, A, Yaro, A 2010, Abdul Amir H. Kadhum, application of some basic corrosion equations for copper-nickel alloy in inhibited acid media, Journal of Tribology and Surface Engineering, 1: 169-183.
Khadom, A, Yaro, 2011, Modelling of Corrosion Inhibition of Copper–Nickel Alloy in Hydrochloric Acid by Benzotriazole, Russian Journal of Physical Chemistry A, 85: 2005-2012.
Maliki, I, Elmsellem, H, Hafez, B, EL, Moussaoui, A, Reda Kachmar, M, Ouahbi, A 2021, The psychological properties of the Arabic BDI-II and the psychological state of the general Moroccan population during the mandatory quarantine due to the COVID-19 pandemic. Caspian Journal of Environmental Sciences, 19: 139-150.
Minhas, S 2020, Could India be the origin of next COVID-19 like epidemic? Science of the Total Environment, 728: 138918.
Moral, H, Aksoy A, Gokcay, F 2008, Modeling of the activated sludge process by using artificial neural networks with automated architecture screening. Comput. Chem. Eng. 32, 2471 – 2478.
Rashid, K.H., Khadom, A.A. 2020, Mathematical Modeling and Electrochemical Behaviour for Corrosion Inhibition of Steel by Kiwi Juice Extract. J Bio Tribo Corros, 6:13.
Rashidi, A 2012, A Galvanostatic modelling for preparation of electrodeposited Nanocrystalline coatings by control of current density. J. Mater. Sci. Technol. 28: 1071-1076.
World Health Organization (WHO) 2020, website. https://covid19.who.int/region/emro/country/iq. Login on 29, June, 2020.
Yaro, AS, Khadom, AA 2010, Polarisation resistance behaviour of corrosion inhibition of low carbon steel in H3PO4 acid. International Journal of Surface Science and Engineering, 4: 429-438.
Zebin Z, Xin, Li, Feng, Liu, Gaofeng, Zhu, Chunfeng, Ma, Liangxu, W 2020, Prediction of the COVID-19 spread in African countries and implications for prevention and control: A case study in South Africa, Egypt, Algeria, Nigeria, Senegal and Kenya, Science of the Total Environment, 729:138959.
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