Mathematical Modeling to Assess the Impact of Covid-19 Transmission in Guyana, South America

Onika Bevny; G Kumar; A V Raja

doi:10.17485/IJST/v16i9.1889

Article

Mathematical Modeling to Assess the Impact of Covid-19 Transmission in Guyana, South America

VIEWS 568
PDF 130

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i9.1889

Year: 2023, Volume: 16, Issue: 9, Pages: 622-631

Original Article

Mathematical Modeling to Assess the Impact of Covid-19 Transmission in Guyana, South America

Onika Bevny^1*, G Kumar², A V Raja³

¹Department of Mathematics, Physics and Statistics, Faculty of Natural Sciences, University of Guyana, Georgetown, Guyana, South America
²Assistant Professor of Statistics, SRM Arts and Science College, Kattankulathur, India
³Senior Lecturer, Department of Mathematics, Physics and Statistics, Faculty of Natural Sciences, University of Guyana, Georgetown, Guyana, South America

*Corresponding Author
Email: [email protected]

Received Date:18 September 2022, Accepted Date:05 February 2023, Published Date:04 March 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: This study aims to find the best mathematical model for modeling the Covid-19 data of Guyana. Methods: The 2-parameter, 3-parameter Weibull distribution, and the Transmuted Weibull Distribution was used to model the Covid-19 data of Guyana using cumulative deaths that occurred on a daily basis from March 12th, 2020 to November 30th, 2021. The Covid-19 data of Guyana was extracted from the ‘ourworldindata’ website. Findings: The transmuted Weibull distribution is the best model for modeling the Covid-19 data of Guyana since it had the lowest AIC value than the other models. Novelty: Several transmuted distributions were developed to model the Covid-19 data of France, the United Kingdom, and Canada. However, in this study, a different transmuted distribution was chosen to model the Covid-19 data of Guyana.

Keywords: Mathematical Modeling; Covid19; Cumulative Deaths; Transmuted Weibull Distribution and Simulation Study

References

Novel Coronavirus (2019-nCov): Situation Report -1. 2020. Available from: https://www.who.int/docs/default-source/wrindia/india-situation-report-1.pdf?sfvrsn=5ca2a672_0
Wang J. Mathematical models for COVID-19: applications, limitations, and potentials. Journal of Public Health and Emergency. 2020;4:9. Available from: https://doi.org/10.21037/jphe-2020-05
Mugisha JYT, Ssebuliba J, Nakakawa JN, Kikawa CR, Ssematimba A. Mathematical modeling of COVID-19 transmission dynamics in Uganda: Implications of complacency and early easing of lockdown. PLOS ONE. 2021;16(2):e0247456. Available from: https://doi.org/10.1371/journal.pone.0247456
Ambikapathy B, Krishnamurthy K. Mathematical Modelling to Assess the Impact of Lockdown on COVID-19 Transmission in India: Model Development and Validation. JMIR Public Health and Surveillance. 2020;6(2):e19368. Available from: https://doi.org/10.2196/19368
Almetwally EM. Application of COVID-19 Pandemic by Using Odd Lomax-G Inverse Weibull Distribution. Mathematical Sciences Letters an International Journal. 2021;10(2):47–57. Available from: https://www.naturalspublishing.com/files/published/56lj72t95ky92p.pdf
Almetwally EM. The Odd Weibull Inverse Topp–Leone Distribution with Applications to COVID-19 Data. Annals of Data Science. 2022;9(1):121–140. Available from: https://doi.org/10.1007/s40745-021-00329-w
Azm WSAE, Almetwally EM, Al-Aziz SN, El-Bagoury AA, Alharbi AH, Abo-Kasem R, et al. A New Transmuted Generalized Lomax Distribution: Properties and Applications to COVID-19 Data. Computational Intelligence and Neuroscience. 2021;2021:1–14. Available from: https://doi.org/10.1155/2021/5918511

Copyright

© 2023 Bevny et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)