In this article we mainly extend a newly introduced deterministic model for the COVID-19 disease to a stochastic setting. More precisely, we incorporated randomness in some coefficients by assuming that they follow a prescribed stochastic dynamics. In this way, the model variables are now represented by stochastic process, that can be simulated by appropriately solving the system of stochastic differential equations. Thus, the model becomes more complete and flexible than the deterministic analogous, as it incorporates additional uncertainties which are present in more realistic situations. In particular, confidence intervals for the main variables and worst case scenarios can be computed.

在本文中，我们主要将新引入的 COVID-19 疾病确定性模型扩展到随机设置。更准确地说，我们通过假设它们遵循规定的随机动力学，将随机性纳入一些系数。这样，模型变量现在由随机过程表示，可以通过适当求解随机微分方程组来模拟。因此，该模型比确定性类比模型更加完整和灵活，因为它包含了在更现实的情况下存在的额外不确定性。特别是，可以计算主要变量和最坏情况的置信区间。