The current effort is devoted to investigating and exploring the stochastic nonlinear mathematical pandemic model to describe the dynamics of the novel coronavirus. The model adopts the form of a nonlinear stochastic susceptible-infected-treated-recovered system, and we investigate the stochastic reproduction dynamics, both analytically and numerically. We applied different standard and nonstandard computational numerical methods for the solution of the stochastic system. The design of a nonstandard computation method for the stochastic system is innovative. Unfortunately, standard computation numerical methods are time-dependent and violate the structure properties of models, such as positivity, boundedness, and dynamical consistency of the stochastic system. To that end, convergence analysis of nonstandard computational methods and simulation with a comparison of standard computational methods are presented.

目前的工作致力于调查和探索随机非线性数学流行病模型来描述新型冠状病毒的动态。该模型采用非线性随机易感-感染-治疗-恢复系统的形式，我们从分析和数值上研究随机再生动力学。我们应用了不同的标准和非标准计算数值方法来求解随机系统。随机系统非标准计算方法的设计具有创新性。不幸的是，标准计算数值方法是时间相关的，并且违反了模型的结构属性，例如随机系统的正性、有界性和动态一致性。为此，