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Modeling the COVID-19 Pandemic Dynamics in Iran and China

Jin Zhao1, Zubair Ahmad2,*, Zahra Almaspoor2

1 School of Finance, Shanghai Lixin University of Accounting and Finance, Shanghai, China
2 Department of Statistics, Yazd University, Yazd, Iran

* Corresponding Author: Zubair Ahmad. Email: email

(This article belongs to this Special Issue: Mathematical aspects of the Coronavirus Disease 2019 (COVID-19): Analysis and Control)

Computers, Materials & Continua 2021, 67(2), 2111-2122. https://doi.org/10.32604/cmc.2021.014259

Abstract

The epidemic outbreak COVID-19 was first detected in the Wuhan city of China and then spread worldwide. It is of great interest to the researchers for its high rate of infection spread and its significant number of fatalities. A detailed scientific analysis of this phenomenon is yet to come. However, it is of interest of governments and other responsible institutions to have the right facts and figures to take every possible necessary action such as an arrangement of the appropriate quarantine activities, estimation of the required number of places in hospitals, assessment of the level of personal protection, and calculating the rate of isolation of infected persons, among others. In this article, we compare the COVID-19 pandemic dynamics between the two most affected Asian countries Iran and mainland China. We provide a convenient method of data comparison that can be helpful for both governmental and private organizations to arrange the appropriate quarantine activities. Furthermore, a statistical model is suggested to provide the best characterization of the COVID-19 daily deaths data of Iran and China. By analyzing daily death events, we observed that the proposed model provides a better description of the COVID-19 events, and therefore, can be used as a good candidate model for predicting them.

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Cite This Article

J. Zhao, Z. Ahmad and Z. Almaspoor, "Modeling the covid-19 pandemic dynamics in iran and china," Computers, Materials & Continua, vol. 67, no.2, pp. 2111–2122, 2021. https://doi.org/10.32604/cmc.2021.014259

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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