Developing algorithms for solving high-dimensional uncertain differential equations has been an exceedingly difficult task. This paper presents an \(\alpha \)-path-based approach that can handle the proposed high-dimensional uncertain SIR model. We apply the \(\alpha \)-path-based approach to calculating the uncertainty distributions and related expected values of the solutions. Furthermore, we employ the method of moments to estimate parameters and design a numerical algorithm to solve them. This model is applied to describing the development trend of COVID-19 using infected and recovered data of Hubei province. The results indicate that lockdown policy achieves almost 100% efficiency after February 13, 2020, which is consistent with the existing literatures. The high-dimensional \(\alpha \)-path-based approach opens up new possibilities in solving high-dimensional uncertain differential equations and new applications.

开发用于求解高维不确定微分方程的算法是一项极其困难的任务。本文提出了一种基于\（\ alpha \）路径的方法，该方法可以处理所提出的高维不确定SIR模型。我们应用\（\阿尔法\） -路径为基础的方法来计算不确定性分布和解决方案相关的预期值。此外，我们采用矩量法估计参数并设计数值算法来求解它们。该模型利用湖北省感染和恢复数据描述了COVID-19的发展趋势。结果表明，锁定策略在2020年2月13日之后达到了几乎100％的效率，这与现有文献一致。高维\（\阿尔法\）为基础的-path方法开辟了解决高维不确定微分方程和新应用的新的可能性。