Stochastic analysis of a COVID-19 model with effects of vaccination and different transition rates: Real data approach

This paper presents a stochastic model for COVID-19 that takes into account factors such as incubation times, vaccine effectiveness, and quarantine periods in the spread of the virus in symptomatically contagious populations. The paper outlines the conditions necessary for the existence and uniqueness of a global solution for the stochastic model. Additionally, the paper employs nonlinear analysis to demonstrate some results on the ergodic aspect of the stochastic model. The model is also simulated and compared to deterministic dynamics. To validate and demonstrate the usefulness of the proposed system, the paper compares the results of the infected class with actual cases from Iraq, Bangladesh, and Croatia. Furthermore, the paper visualizes the impact of vaccination rates and transition rates on the dynamics of infected people in the infected class.

Automated summary

This paper presents a stochastic model that considers various factors in the spread of COVID-19, such as incubation times, vaccine effectiveness, and quarantine periods. The paper outlines the conditions for the existence and uniqueness of a global solution for the model and uses nonlinear analysis to demonstrate some results on its ergodic aspect. The model is simulated and compared to deterministic dynamics, and its usefulness is validated by comparing the results with actual cases from Iraq, Bangladesh, and Croatia. Additionally, the paper visualizes the impact of vaccination rates and transition rates on the dynamics of infected people.