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Research Article | Open Access

Mathematical modeling to study the interactions of two risk populations in COVID-19 spread in Thailand

Siriprapa Ritraksa1Chadaphim Photphanloet1Sherif Eneye Shuaib2Arthit Intarasit1Pakwan Riyapan1( )
Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Pattani 94000, Thailand
Department of Mathematics and Statistics, York University, 4700 Keele Street Toronto, ON M3J1P3, Canada
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Abstract

The use of vaccines has always been controversial. Individuals in society may have different opinions about the benefits of vaccines. As a result, some people decide to get vaccinated, while others decide otherwise. The conflicting opinions about vaccinations have a significant impact on the spread of a disease and the dynamics of an epidemic. This study proposes a mathematical model of COVID-19 to understand the interactions of two populations: the low risk population and the high risk population, with two preventive measures. Unvaccinated individuals with chronic diseases are classified as high risk population while the rest are a low risk population. Preventive measures used by low risk group include vaccination (pharmaceutical way), while for the high risk population they include wearing masks, social distancing and regular hand washing (non-pharmaceutical ways). The susceptible and infected sub-populations in both the low risk and the high risk groups were studied in detail through calculations of the effective reproduction number, model analysis, and numerical simulations. Our results show that the introduction of vaccination in the low risk population will significantly reduce infections in both subgroups.

CLC number: 34A34, 65L20, 65L80, 93A30

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AIMS Mathematics
Pages 2044-2061

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Cite this article:
Ritraksa S, Photphanloet C, Shuaib SE, et al. Mathematical modeling to study the interactions of two risk populations in COVID-19 spread in Thailand. AIMS Mathematics, 2023, 8(1): 2044-2061. https://doi.org/10.3934/math.2023105

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Received: 11 August 2022
Revised: 16 October 2022
Accepted: 18 October 2022
Published: 15 January 2023
©2023 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)