Modeling the dynamics of novel coronavirus (COVID-19) via stochastic epidemic model
Author(s) -
Ghulam Hussain,
Tahir Khan,
Amir Khan,
Mustafa İnç,
Gul Zaman,
Kottakkaran Sooppy Nisar,
Ali Akgül
Publication year - 2021
Publication title -
alexandria engineering journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.584
H-Index - 58
eISSN - 2090-2670
pISSN - 1110-0168
DOI - 10.1016/j.aej.2021.02.036
Subject(s) - uniqueness , lyapunov function , coronavirus , transmission (telecommunications) , covid-19 , statistical physics , stochastic modelling , stochastic process , epidemic model , extinction (optical mineralogy) , mathematics , computer science , physics , mathematical analysis , disease , nonlinear system , population , infectious disease (medical specialty) , statistics , medicine , telecommunications , demography , quantum mechanics , pathology , sociology , optics
Novel coronavirus disease is a burning issue all over the world. Spreading of the novel coronavirus having the characteristic of rapid transmission whenever the air molecules or the freely existed virus includes in the surrounding and therefore the spread of virus follows a stochastic process instead of deterministic. We assume a stochastic model to investigate the transmission dynamics of the novel coronavirus. To do this, we formulate the model according to the charectersitics of the corona virus disease and then prove the existence as well as the uniqueness of the global positive solution to show the well posed-ness and feasibility of the problem. Following the theory of dynamical systems as well as by constructing a suitable stochastic Lyapunov function, we establish sufficient conditions of the extinction and the existence of stationary distribution. Finally, we carry out the large scale numerical simulations to demonstrate the verification of our analytical results.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom