We study the dynamic evolution of COVID-19 cased by the Omicron variant via a fractional susceptible-exposedinfected-removed (SEIR) model. Preliminary data suggest that the symptoms of Omicron infection are not prominent and the transmission is therefore more concealed, which causes a relatively slow increase in the detected cases of the new infected at the beginning of the pandemic. To characterize the specific dynamics, the Caputo-Hadamard fractional derivative is adopted to refined the classical SEIR model. Based on the reported data, we infer the fractional order, timedependent parameters, as well as unobserved dynamics of the fractional SEIR model via fractional physics-informed neural networks (fPINNs). Then, we make short-time predictions using the learned fractional SEIR model.
翻译:初步数据表明,Omicro感染的症状并不明显,因此传播更加隐蔽,从而导致在大流行病开始时新感染病例相对缓慢地增加。为了确定具体动态特征,卡普托-哈达马德分衍生物被采纳来改进传统的SEIR模型。根据所报告的数据,我们推断了分数顺序、时间参数以及通过分数物理学知情神经网络(fPINNSs)的分数 SEIR模型的未观测动态。然后,我们利用所学的分数SEIR模型进行短期预测。