Insights into COVID-19 stochastic modelling with effects of various transmission rates: simulations with real statistical data from UK, Australia, Spain, and India

Abstract

Abstract In the literature [16], the COVID-19 model has been constructed using deterministic approach. The present manuscript examines a stochastic model designed to capture the interplay between COVID-19 and varying infection rates on disease dynamics. We present the necessary criteria for a global solution to the considered model to exist and be unique. To illustrate several outcomes pertaining to the ergodic properties of the given system, the we utilize nonlinear analysis. Furthermore, the model undergoes simulation and is compared with deterministic dynamics. To verify the efficacy of the considered model and demonstrate its utility, we compare the dynamics of the infected population to real statistical data from multiple countries, such as the United Kingdom, Australia, Spain, and India. The proposed model has proven to be a reliable and effective tool for understanding the intricate nature of COVID-19 dynamics. Moreover, we provide a visually striking depiction of the impact of different infection rates on the propagation of the model under investigation. This visualization provides valuable insight into the multifaceted nature of the pandemic and significantly contributes to the comprehension of COVID-19 dynamics.