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

Anees A. Khadom; Ali Khudhair Al-Jiboory; Mustafa S. Mahdi; Hameed B. Mahood

doi:10.22124/cjes.2021.4930

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

Anees A. Khadom^*, Ali Khudhair Al-Jiboory, Mustafa S. Mahdi, Hameed B. Mahood

^*Corresponding author for this work

Chemical Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

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 (2^nd, 3^rd, and 4^th 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.

Original language	English
Pages (from-to)	431-440
Number of pages	10
Journal	Caspian Journal of Environmental Sciences
Volume	19
Issue number	3
DOIs	https://doi.org/10.22124/cjes.2021.4930
Publication status	Published - 31 Jul 2021

Bibliographical note

Publisher Copyright:
© by University of Guilan, Printed in I.R. Iran.

Keywords

Confirmed cases
COVID-19
Mathematical modelling
Neural networks
Statistical analysis

ASJC Scopus subject areas

Ecology, Evolution, Behavior and Systematics
Agricultural and Biological Sciences (miscellaneous)
General Environmental Science

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10.22124/cjes.2021.4930

Cite this

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title = "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",

abstract = "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.",

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author = "Khadom, {Anees A.} and Al-Jiboory, {Ali Khudhair} and Mahdi, {Mustafa S.} and Mahood, {Hameed B.}",

note = "Publisher Copyright: {\textcopyright} by University of Guilan, Printed in I.R. Iran.",

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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. / Khadom, Anees A.; Al-Jiboory, Ali Khudhair; Mahdi, Mustafa S. et al.
In: Caspian Journal of Environmental Sciences, Vol. 19, No. 3, 31.07.2021, p. 431-440.

Research output: Contribution to journal › Article › peer-review

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T2 - A case of the first six months of 2020

AU - Khadom, Anees A.

AU - Al-Jiboory, Ali Khudhair

AU - Mahdi, Mustafa S.

AU - Mahood, Hameed B.

N1 - Publisher Copyright: © by University of Guilan, Printed in I.R. Iran.

PY - 2021/7/31

Y1 - 2021/7/31

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AB - 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.

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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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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