Abstract
The COVID-19 pandemic has caused emotional loss to people around the world and provides an unusual test for public welfare, educational framework, food frameworks, and the world of work. The economic and social turmoil caused by this epidemic is increasing, and many people are at risk of falling into oppressive poverty. In this article, we describe the pandemic of infectious illness with the help of stochastic mathematical modeling. Based on the environmental white noise and by building appropriate Lyapunov functions and by applying Ito’s formula, a few subjective properties are gotten. We provide a new mathematical model for the COVID-19 spread. The novel stochastic model is used to analyze the existence and prevalence of the disease, as well as its extinction. A numerical approach is developed for computing approximate solutions of the model. We show numerical simulations of deterministic and stochastic models of COVID-19 by utilizing the MATLAB software. In this direction, three graphs are included in the paper for the numerical interpretation of the stochastic model with the help of existing parametric and initial values for the model.
Highlights
In this part of the research, we provide a numerical scheme for the suggested stochastic COVID-19 epidemic model (1)
R∗k+1 = R∗k + ðγI ∗k − yR∗ÞΔt + σ2I ∗k℘kpffiΔffiffiffitffi + σ222 I ∗kÀ℘2k − 1ÁΔt: ð49Þ. This scheme is utilized for the numerical results of the stochastic model (1)
The numerical simulations are presented with the help of the graphs
Summary
A new virus known as “corona” was claimed to be wreaking havoc on the Chinese city of Wuhan in December 2019. This virus and its subsequent pandemic initially struck Wuhan and spread to practically the entire world [1, 2]. In battling the fatal pandemic, health researchers, government politicians, and health care authorities are perplexed. They all have a different perspective on the problem and are working hard to reduce the number of people who die as a result of the outbreak. One can see the useful work in [5, 6]
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