Analysis of a multiply delayed stochastic Ebola model based on probability density function with case study

In this study, we introduce a new version of the SIQC epidemic model, focusing on Ebola infection dynamics and emphasizing time-delay effects. We have demonstrated the existence of a unique solution for the proposed stochastic model through rigorous mathematical derivations. Furthermore, by developing a stochastic Lyapunov function, we assess the extinction and persistence of the proposed stochastic model. We fit the model to available Ebola virus data in Western Guinea from the first forty weeks of the 2015 Ebola outbreak using the conventional curve fitting method, and the model parameters were calculated as a result. To reinforce our findings, we provide computer simulations as supplementary evidence. Based on the simulation findings using the stochastic SIQC model with time delay, it is determined that perturbations and time delay greatly impact the risk of Ebola virus transmission.