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Conformable mathematical modeling of the COVID-19 transmission dynamics: A more general study
  • Hayman Thabet,
  • Subhash Kendre
Hayman Thabet
Brown University

Corresponding Author:[email protected]

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Subhash Kendre
Savitribai Phule Pune University
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Many challenges are still faced in bridging the gap between Mathematical modeling and biological sciences. Measuring population immunity to assess the epidemiology of health and disease is a challenging task and is currently an active area of research. However, to meet these challenges, mathematical modeling is an effective technique in shaping the population dynamics that can help disease control. In this paper, we introduce a Susceptible-Infected-Recovered (SIR) model and a Susceptible-Infected-Recovered-Exposed-Deceased (SEIRD) model based on conformable space-time PDEs for the Coronavirus Disease 2019 (COVID-19) pandemic. As efficient analytical tools, we present new modifications based on the fractional exponential rational function method (ERFM) and an analytical technique based on the Adomian decomposition method for obtaining the solutions for the proposed models. These analytical approaches are more efficious for obtaining analytical solutions for nonlinear systems of partial differential equations (PDEs) with conformable derivatives. The interesting result of this paper is that it yields new exact and approximate solutions to the proposed COVID-19 pandemic models with conformable space-time partial derivatives
21 Dec 2022Submitted to Mathematical Methods in the Applied Sciences
21 Dec 2022Submission Checks Completed
21 Dec 2022Assigned to Editor
26 Dec 2022Review(s) Completed, Editorial Evaluation Pending
19 Jan 2023Reviewer(s) Assigned
05 Apr 2023Editorial Decision: Revise Major
14 Apr 20231st Revision Received
14 Apr 2023Submission Checks Completed
14 Apr 2023Assigned to Editor
14 Apr 2023Review(s) Completed, Editorial Evaluation Pending
28 Apr 2023Reviewer(s) Assigned
28 Jun 2023Editorial Decision: Accept