Open Access

ARTICLE

Stochastic Epidemic Model of Covid-19 via the Reservoir-People Transmission Network

Kazem Nouri^1,*, Milad Fahimi¹, Leila Torkzadeh¹, Dumitru Baleanu^2,3

1 Department of Mathematics, Faculty of Mathematics, Statistics and Computer Sciences, Semnan University, P. O. Box 35195-363, Semnan, Iran
2 Department of Mathematics, Faculty of Arts and Sciences, Cankaya University, Ankara, Turkey
3 Institute of Space Sciences, Magurele–Bucharest, Romania

* Corresponding Authors: Kazem Nouri. Email: ,

Computers, Materials & Continua 2022, 72(1), 1495-1514. https://doi.org/10.32604/cmc.2022.024406

Received 16 October 2021; Accepted 17 December 2021; Issue published 24 February 2022

Abstract

The novel Coronavirus COVID-19 emerged in Wuhan, China in December 2019. COVID-19 has rapidly spread among human populations and other mammals. The outbreak of COVID-19 has become a global challenge. Mathematical models of epidemiological systems enable studying and predicting the potential spread of disease. Modeling and predicting the evolution of COVID-19 epidemics in near real-time is a scientific challenge, this requires a deep understanding of the dynamics of pandemics and the possibility that the diffusion process can be completely random. In this paper, we develop and analyze a model to simulate the Coronavirus transmission dynamics based on Reservoir-People transmission network. When faced with a potential outbreak, decision-makers need to be able to trust mathematical models for their decision-making processes. One of the most considerable characteristics of COVID-19 is its different behaviors in various countries and regions, or even in different individuals, which can be a sign of uncertain and accidental behavior in the disease outbreak. This trait reflects the existence of the capacity of transmitting perturbations across its domains. We construct a stochastic environment because of parameters random essence and introduce a stochastic version of the Reservoir-People model. Then we prove the uniqueness and existence of the solution on the stochastic model. Moreover, the equilibria of the system are considered. Also, we establish the extinction of the disease under some suitable conditions. Finally, some numerical simulation and comparison are carried out to validate the theoretical results and the possibility of comparability of the stochastic model with the deterministic model.

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Keywords

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