The present study is conducted to analyse the computational dynamical analysis of the stochastic susceptible-infected-recovered pandemic model of the novel coronavirus. We adopted two ways for stochastic modelling like as transition probabilities and parametric perturbation techniques. We applied different and well-known computational methods like Euler Maruyama, stochastic Euler, and stochastic Runge Kutta to study the dynamics of the model mentioned above. Unfortunately, these computational methods do not restore the dynamical properties of the model like positivity, boundedness, consistency, and stability in the sense of biological reasoning, as desired. Then, for the given stochastic model, we developed a stochastic non-standard finite difference method. Following that, several theorems are presented to support the proposed method, which is shown to satisfy all of the model's dynamical properties. To that end, several simulations are presented to compare the proposed method's efficiency to that of existing stochastic methods.

本研究旨在分析新型冠状病毒随机易感-感染-康复大流行模型的计算动力学分析。我们采用了两种方法进行随机建模，如转移概率和参数扰动技术。我们应用了不同的知名计算方法，如 Euler Maruyama、随机 Euler 和随机 Runge Kutta 来研究上述模型的动力学。不幸的是，这些计算方法并没有根据需要恢复模型的动态特性，如生物推理意义上的积极性、有界性、一致性和稳定性。然后，对于给定的随机模型，我们开发了一种随机非标准有限差分方法。接下来，提出了几个定理来支持所提出的方法，这表明满足模型的所有动态特性。为此，提出了几个模拟来比较所提出方法的效率与现有随机方法的效率。