COVID-19 and underlying health conditions: A modeling investigation

Chayu Yang; Jin Wang; Chayu Yang; Jin Wang

doi:10.3934/mbe.2021191

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 4: 3790-3812. doi: 10.3934/mbe.2021191

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Research article

COVID-19 and underlying health conditions: A modeling investigation

Chayu Yang ¹,
Jin Wang ^{2
,
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1.
Department of Mathematics, University of Florida, Gainesville, FL 32607, USA
2.
Department of Mathematics, University of Tennessee at Chattanooga, 615 McCallie Ave., Chattanooga, TN 37403, USA

Academic Editor: Yang Kuang

Received: 07 March 2021 Accepted: 20 April 2021 Published: 30 April 2021

We propose a mathematical model based on a system of differential equations, which incorporates the impact of the chronic health conditions of the host population, to investigate the transmission dynamics of COVID-19. The model divides the total population into two groups, depending on whether they have underlying conditions, and describes the disease transmission both within and between the groups. As an application of this model, we perform a case study for Hamilton County, the fourth-most populous county in the US state of Tennessee and a region with high prevalence of chronic conditions. Our data fitting and simulation results quantify the high risk of COVID-19 for the population group with underlying health conditions. The findings suggest that weakening the disease transmission route between the exposed and susceptible individuals, including the reduction of the between-group contact, would be an effective approach to protect the most vulnerable people in this population group.
- COVID-19 transmission,
- chronic conditions,
- data fitting
Citation: Chayu Yang, Jin Wang. COVID-19 and underlying health conditions: A modeling investigation[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 3790-3812. doi: 10.3934/mbe.2021191

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Abstract

We propose a mathematical model based on a system of differential equations, which incorporates the impact of the chronic health conditions of the host population, to investigate the transmission dynamics of COVID-19. The model divides the total population into two groups, depending on whether they have underlying conditions, and describes the disease transmission both within and between the groups. As an application of this model, we perform a case study for Hamilton County, the fourth-most populous county in the US state of Tennessee and a region with high prevalence of chronic conditions. Our data fitting and simulation results quantify the high risk of COVID-19 for the population group with underlying health conditions. The findings suggest that weakening the disease transmission route between the exposed and susceptible individuals, including the reduction of the between-group contact, would be an effective approach to protect the most vulnerable people in this population group.

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