journal article·2021Open access

Modeling the COVID-19 Pandemic Dynamics in Iran and China

Jin Zhao, Zubair Ahmad, Zahra Almaspoor

Computers, Materials & Continua· Volume 67· Issue 2

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.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · SARS-CoV-2 and COVID-19 Research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (16)

Show source-provided references
1. Detection of 2019 novel coronavirus by real-time RT-PCR10.2807/1560-7917.ES.2020.25.3.2000045 ↗
2. Comparison of the coronavirus epidemic dynamics in Italy and mainland China
3. Comparison of the coronavirus pandemic dynamics in Europe, USA and South Korea
4. Coronavirus epidemic outbreak in Europe: Comparison with the dynamics in mainland China10.20535/kpi-sn.2020.1.198043 ↗
5. Estimation of time-varying reproduction numbers underlying epidemiological processes: A new statistical tool for the COVID-19 pandemic10.1371/journal.pone.0236464 ↗
6. Simulations and predictions of COVID-19 pandemic with the use of SIR model10.20535/ibb.2020.4.2.204274 ↗
7. Statistics-based predictions of coronavirus epidemic spreading in mainland China10.20535/ibb.2020.4.1.195074 ↗
8. The reproductive number of COVID-19 is higher compared to SARS coronavirus
9. Comparison of COVID-19 pandemic dynamics in Asian countries with statistical modeling
10. Comparison of mathematical models for the dynamics of the Chernivtsi children disease10.1016/j.matcom.2016.01.003 ↗
11. A time delay dynamical model for outbreak of 2019-nCoV and the parameter identification10.1515/jiip-2020-0010 ↗
12. Effective containment explains sub exponential growth in recent confirmed COVID-19 cases in China10.1126/science.abb4557 ↗
13. Investigating the cases of novel coronavirus disease in China using dynamic statistical techniques10.1016/j.heliyon.2020.e03747 ↗
14. New methods to define heavy-tailed distributions with applications to insurance data10.1080/16583655.2020.1741942 ↗
15. A new method for generating families of continuous distributions10.1007/s40300-013-0007-y ↗
16. Recent developments in distribution theory: A brief survey and some new generalized classes of distributions10.18187/pjsor.v15i1.2803 ↗