A novel fractional order model of SARS-CoV-2 and Cholera disease with real data

ÖZKÖSE F., Habbireeh R., ŞENEL M. T.

Journal of Computational and Applied Mathematics, vol.423, 2023 (SCI-Expanded, Scopus)

  • Publication Type: Article / Article
  • Volume: 423
  • Publication Date: 2023
  • Doi Number: 10.1016/j.cam.2022.114969
  • Journal Name: Journal of Computational and Applied Mathematics
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, MathSciNet, Metadex, zbMATH, DIALNET, Civil Engineering Abstracts
  • Keywords: SARS-CoV-2 and Cholera disease, Fractional-order derivative, Stability analysis, Real data, Parameter estimation, Sensitivity analysis
  • Erciyes University Affiliated: Yes

Abstract

© 2022 Elsevier B.V.This study presents a novel approach to investigating COVID-19 and Cholera disease. In this situation, a fractional-order model is created to investigate the COVID-19 and Cholera outbreaks in Congo. The existence, uniqueness, positivity, and boundedness of the solution are studied. The equilibrium points and their stability conditions are achieved. Subsequently, the basic reproduction number (the virus transmission coefficient) is calculated that simply refers to the number of people, to whom an infected person can make infected, as R0=6.7442389e−10 by using the next generation matrix method. Next, the sensitivity analysis of the parameters is performed according to R0. To determine the values of the parameters in the model, the least squares curve fitting method is utilized. A total of 22 parameter values in the model are estimated by using real Cholera data from Congo. Finally, to find out the dynamic behavior of the system, numerical simulations are presented. The outcome of the study indicates that the severity of the Cholera epidemic cases will decrease with the decrease in cases of COVID-19, through the implementation and follow-up of safety measures that have been taken to reduce COVID-19 cases.