Modeling and Predicting Corona Contagion Dynamics in China, USA, Brazil & Ethiopia
Science Journal of Applied Mathematics and Statistics
Volume 8, Issue 5, October 2020, Pages: 67-72
Received: Aug. 13, 2020; Accepted: Sep. 8, 2020; Published: Sep. 28, 2020
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Authors
Thomas Wetere Tulu,
Ieng Tak Leong,
Zunyou Wu,
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Abstract
The COVID-19 pandemic is a global pandemic of coronavirus disease 2019, caused by severe acute respiratory syndrome coronavirus 2 (SARS CoV 2). The outbreak was first identified in Wuhan, China, in December 2019. In this article, we investigate the problem of modelling the trend of the current Coronavirus disease 2019 pandemic in China, USA, Ethiopia and Brazil along time. Two different models were developed using Bayesian Markov chain Monte Carlo simulation methods. The models fitted included Poisson autoregressive as a function of a short-term dependence only and Poisson autoregressive as a function of both a short-term dependence and a long-term dependence. The models can be employed to understand the contagion dynamics of the COVID-19, which can heavily impact health, economy and finance. The result indicates whether disease has an upward/downward trend, and where about every country is on that trend, all of which can help the public decision-makers to better plan health policy interventions and take the appropriate actions to control the spreading of the virus.
Keywords
Mathematical Modeling, Poisson Autoregressive, COVID-19, Markov Chain Monte Carlo, Simulation
To cite this article
Thomas Wetere Tulu, Ieng Tak Leong, Zunyou Wu, Modeling and Predicting Corona Contagion Dynamics in China, USA, Brazil & Ethiopia, Science Journal of Applied Mathematics and Statistics. Vol. 8, No. 5, 2020, pp. 67-72. doi: 10.11648/j.sjams.20200805.13
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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