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An SEQAIHR model to study COVID-19 transmission and optimal control strategies in Hong Kong, 2022.
Saha, Pritam; Biswas, Sudhanshu Kumar; Biswas, Md Haider Ali; Ghosh, Uttam.
  • Saha P; Department of Applied Mathematics, University of Calcutta, Kolkata, 700009 India.
  • Biswas SK; Department of Mathematics, Sripat Singh College, Murshidabad, India.
  • Biswas MHA; Mathematics Discipline, Khulna University, Khulna, 9208 Bangladesh.
  • Ghosh U; Department of Applied Mathematics, University of Calcutta, Kolkata, 700009 India.
Nonlinear Dyn ; 111(7): 6873-6893, 2023.
Article in English | MEDLINE | ID: covidwho-2244792
ABSTRACT
During the COVID-19 pandemic, one of the major concerns was a medical emergency in human society. Therefore it was necessary to control or restrict the disease spreading among populations in any fruitful way at that time. To frame out a proper policy for controlling COVID-19 spreading with limited medical facilities, here we propose an SEQAIHR model having saturated treatment. We check biological feasibility of model solutions and compute the basic reproduction number ( R 0 ). Moreover, the model exhibits transcritical, backward bifurcation and forward bifurcation with hysteresis with respect to different parameters under some restrictions. Further to validate the model, we fit it with real COVID-19 infected data of Hong Kong from 19th December, 2021 to 3rd April, 2022 and estimate model parameters. Applying sensitivity analysis, we find out the most sensitive parameters that have an effect on R 0 . We estimate R 0 using actual initial growth data of COVID-19 and calculate effective reproduction number for same period. Finally, an optimal control problem has been proposed considering effective vaccination and saturated treatment for hospitalized class to decrease density of the infected class and to minimize implemented cost.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Topics: Vaccines Language: English Journal: Nonlinear Dyn Year: 2023 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Topics: Vaccines Language: English Journal: Nonlinear Dyn Year: 2023 Document Type: Article