International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 10, Issue 3 (March 2023), Pages: 108-113

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 Original Research Paper

 Probabilistic analysis of COVID-19 transmission in Kenya using Markov chain

 Author(s): 

 Joseph M. Mugambi *, Edwin B. Atitwa, Zakayo N. Morris, Maurice Wanyonyi

 Affiliation(s):

 Department of Mathematics and Statistics, University of Embu, Embu, Kenya

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-4062-7505

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2023.03.014

 Abstract:

Since the outbreak of the COVID-19 pandemic, many countries have continued to suffer economically due to trade losses. COVID-19 has evolved into different forms and hence became a problem to analyze its transmission. As a result of increased COVID-19 infections, there has been a scarcity of resources like hospital facilities, quarantine centers, and personal protective equipment (PPEs) for the medics. Therefore, accurate planning has to be made by the government of Kenya to ensure that resources are made available to combat the rising COVID-19 cases. To ensure effective future planning for the COVID-19 pandemic, proper analysis of the COVID-19 pandemic among the population is key. Therefore, this study will go a long way in providing insights on how to plan for the Kenyan population through probabilistic analysis of the COVID-19 pandemic using the Markov chain. The study used Secondary Cumulative data from the Kenya ministry of health for a period between 1st June 2021 and 1st May 2022. The data was analyzed using a steady-state Markov chain in which the transition probability matrix for the COVID-19 pandemic was computed. The number of individuals infected by the COVID-19 virus and who recovered at the end of the study period was set at zero since COVID-19 disease is not curable. The results were presented in the table and reported at a 95% confidence level. Based on the findings, the study concluded that a steady-state Markov chain is beneficial in simulating the coronavirus infection in numerous stages. Also, it is noted that the use of the steady-state Markov chain model allows for capturing short and long-term memory effects that greatly improve the estimation of the number of new cases of COVID-19 and indicate whether the disease has an upward/downward trend.

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: COVID-19 transmission, Probabilistic analysis, Markov chain model, Transition probability matrix

 Article History: Received 19 September 2022, Received in revised form 11 December 2022, Accepted 14 December 2022

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Mugambi JM, Atitwa EB, Morris ZN, and Wanyonyi M (2023). Probabilistic analysis of COVID-19 transmission in Kenya using Markov chain. International Journal of Advanced and Applied Sciences, 10(3): 108-113

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 Figures

 Fig. 1 Fig. 2 

 Tables

 Table 1 

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