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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Thomas Wetere Tulu,
Ieng Tak Leong,
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.
Thomas Wetere Tulu,
Ieng Tak Leong,
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.
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.
Balkhair AA. COVID-19 Pandemic: A New Chapter in the History of Infectious Diseases. Oman Med J. 2020; 35 (2): e123.
Yunlu Wang, Menghan Hu, Qingli Li, Xiao-Ping Zhang, Guangtao Zhai, Nan Yao. Abnormal respiratory patterns classier may contribute to large-scale screening of people infected with COVID19 in an accurate and unobtrusive manner. arXiv preprint arXiv: 2002.05534; 2020.
Zixin Hu, Qiyang Ge, Li Jin, Momiao Xiong. Artificial intelligence forecasting of COVID-19 in China. arXiv preprint arXiv: 2002.07112; 2020.
Fan Wu, Su Zhao, Bin Yu, Yan-Mei Chen, Wen Wang, Zhi-Gang Song, Yi Hu, Zhao-Wu Tao, Jun Hua Tian, Yuan-Yuan Pei, et al. A new coronavirus associated with human respiratory disease in china. Nature. 2020; 579 (7798): 265–269.
World Health Organization. Global surveillance for COVID-19 disease caused by human infection with the 2019 novel coronavirus, interim guidance; 2020.
Spiegelhatler, D. J., Thomas, A., Best, N. G., & Lunn, D. (2003). WinBUGS User manual: Version 1.4. Cambridge: MRC Biostatistics Unit.
WHO: Coronavirus Cases.: //covid19.who.int/?gclid=EAIaIQobChMI4saYtNe_6gIVB7eWCh0uRAcgEAAYASAAEgK8O_D_BwE.
Agosto, A. and Giudici, P, A Poisson Autoregressive Model to Understand COVID-19 Contagion Dynamics (March 9, 2020). Available at SSRN: https://ssrn.com/abstract=3551626.
Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulationusing multiple sequences. Statistical Sciences, 7, 457–472.
WHO. Coronavirus disease (COVID-19) situation report 165. Geneva: World Health Organization. July 3, 2020. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200703-covid-19-sitrep-165.pdf?sfvrsn=b27a772e_6 (accessed July 3, 2020).
Rosenberg ES, Dufort EM, Udo T, et al. Association of treatment with hydroxychloroquine or azithromycin with in-hospital mortality in patients with COVID-19 in New York State. JAMA 2020; 323: 2493–502.
Arabi YM, Deeb AM, Al-Hameed F, et al. Macrolides in critically ill patients with Middle East respiratory syndrome. Int J Infect Dis2019; 81: 184–90.
Cao B, Wang Y, Wen D, et al. A trial of lopinavir-ritonavir in adults hospitalized with severe Covid-19. N Engl J Med 2020; 382: 1787–99.
Marijon E, Karam N, Jost D, et al. Out-of-hospital cardiac arrest during the COVID-19 pandemic in Paris, France: a population-based, observational study. The Lancet Public Health.
Worldometer. COVID-19 coronavirus pandemic. 2020. https://www.worldometers.info/coronavirus/ (accessed May 28, 2020).