Abstract
The current effort is devoted to investigating and exploring the stochastic nonlinear mathematical pandemic model to describe the dynamics of the novel coronavirus. The model adopts the form of a nonlinear stochastic susceptible-infected-treated-recovered system, and we investigate the stochastic reproduction dynamics, both analytically and numerically. We applied different standard and nonstandard computational numerical methods for the solution of the stochastic system. The design of a nonstandard computation method for the stochastic system is innovative. Unfortunately, standard computation numerical methods are time-dependent and violate the structure properties of models, such as positivity, boundedness, and dynamical consistency of the stochastic system. To that end, convergence analysis of nonstandard computational methods and simulation with a comparison of standard computational methods are presented.
Highlights
Humanity is enduring many diseases of variable lethality since its birth
Scientists tried very hard to build instruments to encounter the adverse effects of ailments and to produce possible treatment via vaccine or medicine
(2020) 2020:505 diseases, COVID-19 has uprooted the humanity by killing many of people and is still consuming many lives to date
Summary
Humanity is enduring many diseases of variable lethality since its birth. Ebola, HIV, and Lassa fever are just a few of them. Shatanawi et al Advances in Difference Equations (2020) 2020:505 diseases, COVID-19 has uprooted the humanity by killing many of people and is still consuming many lives to date It was first discovered in the city Wuhan, Province Hubei in China [1, 2]. Every continent is a sufferer, and among them, China, Iran, the UK, the USA, Spain, Italy are considered the most effected countries Major symptoms of this disease include lethargy, dry cough, followed by fever [4]. We aim to suggest and present mathematical analysis revealing the spread of such a deathly disease, and develop some prediction with real-world data [15, 16].
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