Impact of high-risk and low-risk population on COVID-19 dynamics considering antimicrobial resistance and control strategies
Department of Integrative Biology, University of Texas at Austin, Austin, Texas, USA
2 Division of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, India
Accepted: 28 July 2023
Published online: 9 August 2023
The WHO declared 5th pandemic, COVID-19, which was first reported in the late 2019, has been coexisting with several other infectious diseases and have claimed millions of lives worldwide. Over the years, this deadly virus has mutated to several variants with different degree of pathogenicity. Individuals with comorbidities are at higher risk of infection and suffering when contracted the virus. In this study, we develop a deterministic model considering high-risk and low-risk susceptible population to study the impact on COVID-19 disease transmission. The model framed is an extension of SEIR model with inclusion of hospitalized of under supervision individuals. Due to interaction between high-risk and low-risk population cross infection is considered in the model. Equilibrium points and basic reproduction number are obtained, and sensitivity analysis on the latter is performed. The model is then modified to see the impact of antimicrobial resistance in the infected population of both groups. The developed model is fitted with the COVID-19 data of Brazil, and few significant parameters have been estimated along with short-term predictions. Finally, the model is extended to optimal control model incorporating control strategy to reduce the infection rate through vaccination and other non-pharmaceutical interventions and see the overall impact on the infected population with implementation of these control strategies. From these analyses though control strategies will help in reducing the spread of disease, with antimicrobial resistance in the run, the recovery rate can be impacted thereby leading to prolonged treatment rate.
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