Modeling and Predicting Corona Contagion Dynamics in China, USA, Brazil & Ethiopia

Thomas Wetere Tulu; Ieng Tak Leong; Zunyou Wu

doi:doi:10.11648/j.sjams.20200805.13

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Modeling and Predicting Corona Contagion Dynamics in China, USA, Brazil & Ethiopia

Thomas Wetere Tulu, Ieng Tak Leong, Zunyou Wu

Published in Science Journal of Applied Mathematics and Statistics (Volume 8, Issue 5)

Received: 13 August 2020 Accepted: 8 September 2020 Published: 28 September 2020

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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.

Published in	Science Journal of Applied Mathematics and Statistics (Volume 8, Issue 5)
DOI	10.11648/j.sjams.20200805.13
Page(s)	67-72
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Mathematical Modeling, Poisson Autoregressive, COVID-19, Markov Chain Monte Carlo, Simulation

References

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[8]	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.
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[15]	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.
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APA Style

Thomas Wetere Tulu, Ieng Tak Leong, Zunyou Wu. (2020). Modeling and Predicting Corona Contagion Dynamics in China, USA, Brazil & Ethiopia. Science Journal of Applied Mathematics and Statistics, 8(5), 67-72. https://doi.org/10.11648/j.sjams.20200805.13

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ACS Style

Thomas Wetere Tulu; Ieng Tak Leong; Zunyou Wu. Modeling and Predicting Corona Contagion Dynamics in China, USA, Brazil & Ethiopia. Sci. J. Appl. Math. Stat. 2020, 8(5), 67-72. doi: 10.11648/j.sjams.20200805.13

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AMA Style

Thomas Wetere Tulu, Ieng Tak Leong, Zunyou Wu. Modeling and Predicting Corona Contagion Dynamics in China, USA, Brazil & Ethiopia. Sci J Appl Math Stat. 2020;8(5):67-72. doi: 10.11648/j.sjams.20200805.13

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@article{10.11648/j.sjams.20200805.13,
  author = {Thomas Wetere Tulu and Ieng Tak Leong and Zunyou Wu},
  title = {Modeling and Predicting Corona Contagion Dynamics in China, USA, Brazil & Ethiopia},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {8},
  number = {5},
  pages = {67-72},
  doi = {10.11648/j.sjams.20200805.13},
  url = {https://doi.org/10.11648/j.sjams.20200805.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20200805.13},
  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.},
 year = {2020}
}

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TY  - JOUR
T1  - Modeling and Predicting Corona Contagion Dynamics in China, USA, Brazil & Ethiopia
AU  - Thomas Wetere Tulu
AU  - Ieng Tak Leong
AU  - Zunyou Wu
Y1  - 2020/09/28
PY  - 2020
N1  - https://doi.org/10.11648/j.sjams.20200805.13
DO  - 10.11648/j.sjams.20200805.13
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 67
EP  - 72
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20200805.13
AB  - 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.
VL  - 8
IS  - 5
ER  -

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Author Information

Thomas Wetere Tulu
Ieng Tak Leong
Zunyou Wu

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Plain Text BibTeX RIS

APA Style

Thomas Wetere Tulu, Ieng Tak Leong, Zunyou Wu. (2020). Modeling and Predicting Corona Contagion Dynamics in China, USA, Brazil & Ethiopia. Science Journal of Applied Mathematics and Statistics, 8(5), 67-72. https://doi.org/10.11648/j.sjams.20200805.13

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ACS Style

Thomas Wetere Tulu; Ieng Tak Leong; Zunyou Wu. Modeling and Predicting Corona Contagion Dynamics in China, USA, Brazil & Ethiopia. Sci. J. Appl. Math. Stat. 2020, 8(5), 67-72. doi: 10.11648/j.sjams.20200805.13

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AMA Style

Thomas Wetere Tulu, Ieng Tak Leong, Zunyou Wu. Modeling and Predicting Corona Contagion Dynamics in China, USA, Brazil & Ethiopia. Sci J Appl Math Stat. 2020;8(5):67-72. doi: 10.11648/j.sjams.20200805.13

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@article{10.11648/j.sjams.20200805.13,
  author = {Thomas Wetere Tulu and Ieng Tak Leong and Zunyou Wu},
  title = {Modeling and Predicting Corona Contagion Dynamics in China, USA, Brazil & Ethiopia},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {8},
  number = {5},
  pages = {67-72},
  doi = {10.11648/j.sjams.20200805.13},
  url = {https://doi.org/10.11648/j.sjams.20200805.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20200805.13},
  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.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Modeling and Predicting Corona Contagion Dynamics in China, USA, Brazil & Ethiopia
AU  - Thomas Wetere Tulu
AU  - Ieng Tak Leong
AU  - Zunyou Wu
Y1  - 2020/09/28
PY  - 2020
N1  - https://doi.org/10.11648/j.sjams.20200805.13
DO  - 10.11648/j.sjams.20200805.13
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 67
EP  - 72
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20200805.13
AB  - 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.
VL  - 8
IS  - 5
ER  -

Copy | Download