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Mathematical analysis of stochastic epidemic model of MERS-corona & application of ergodic theory.
Hussain, Shah; Tunç, Osman; Rahman, Ghaus Ur; Khan, Hasib; Nadia, Elissa.
Afiliação
  • Hussain S; Faculty of Informatics & Computing, Universiti Sultan Zainal Abidin, Besut Campus, Terengganu, Malaysia.
  • Tunç O; Department of Computer Programming, Baskale Vocational School, Van Yuzuncu Yil University, 65080, Van, Turkey.
  • Rahman GU; Department of Mathematics and Statistics, University of Swat, District Swat, Pakistan.
  • Khan H; Department of Mathematics and Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia.
  • Nadia E; Faculty of Informatics & Computing, Universiti Sultan Zainal Abidin, Besut Campus, Terengganu, Malaysia.
Math Comput Simul ; 207: 130-150, 2023 May.
Article em En | MEDLINE | ID: mdl-36618952
The "Middle East Respiratory" (MERS-Cov) is among the world's dangerous diseases that still exist. Presently it is a threat to Arab countries, but it is a horrible prediction that it may propagate like COVID-19. In this article, a stochastic version of the epidemic model, MERS-Cov, is presented. Initially, a mathematical form is given to the dynamics of the disease while incorporating some unpredictable factors. The study of the underlying model shows the existence of positive global solution. Formulating appropriate Lyapunov functionals, the paper will also explore parametric conditions which will lead to the extinction of the disease from a community. Moreover, to reveal that the infection will persist, ergodic stationary distribution will be carried out. It will also be shown that a threshold quantity exists, which will determine some essential parameters for exploring other dynamical aspects of the main model. With the addition of some examples, the underlying stochastic model of MERS-Cov will be studied graphically for more illustration.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article