论文标题
随机$θ$ -Seihrd模型：在COVID-19 verral中添加随机性
A stochastic $θ$-SEIHRD model: adding randomness to the COVID-19 spread
论文作者
论文摘要
在本文中，我们主要将[10]中开发的确定性模型扩展到随机设置。更确切地说，我们假设它们遵循规定的随机动力学，将随机性纳入了某些系数。通过这种方式，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的系统来模拟。因此，该模型比确定性类似物变得更完整，更灵活，因为它结合了在更现实的情况下存在的其他不确定性。特别是，可以计算主要变量和最坏情况的置信区间。
In this article we mainly extend the deterministic model developed in [10] 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 solve 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.
