Regression and validation studies of the spread of novel COVID-۱۹ in Iraq using mathematical and dynamic neural networks models: A case of the first six months of ۲۰۲۰
- سال انتشار: 1400
- محل انتشار: مجله علوم زیستی خاورمیانه، دوره: 19، شماره: 3
- کد COI اختصاصی: JR_CJES-19-3_006
- زبان مقاله: انگلیسی
- تعداد مشاهده: 136
نویسندگان
Department of Chemical Engineering, College of Engineering – University of Diyala – Baquba City ۳۲۰۰۱, Diyala governorate, Iraq
Department of Mechanical Engineering, College of Engineering – University of Diyala – Baquba City ۳۲۰۰۱, Diyala governorate, Iraq
Department of Chemical Engineering, College of Engineering – University of Diyala – Baquba City ۳۲۰۰۱, Diyala governorate, Iraq
Department of Chemical and Process Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU۲ ۷XH, UK
چکیده
The dramatic spread of COVID-۱۹ has put the entire world at risk. In this work, the spread of COVID-۱۹ 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 ۲۵, February to ۱۸, June ۲۰۲۰ was used for regression, validation, and model construction of Dynamic Neural Networks (DNN). Nine samples (۱۹ – ۲۷ June ۲۰۲۰) 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 ۲۰۲۰. Three mathematical models are proposed. Linear, polynomials (۲nd, ۳rd, and ۴th 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 ۰.۹۹۶۴ and ۰.۹۹۷۴ 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 ۵۰۰۰۰ and ۲۰۰۰ respectively at the end of June ۲۰۲۰.کلیدواژه ها
Mathematical modelling, COVID-۱۹, Statistical analysis, Confirmed cases, Neural networksاطلاعات بیشتر در مورد COI
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