Department of Mathematics, Shanghai University, 99 Shangda Road, Shanghai 200444, China.
Division of Applied Mathematics, Brown University, 170 Hope Street, Providence, Rhode Island 02906, USA.
Chaos. 2022 Jul;32(7):071101. doi: 10.1063/5.0099450.
We study the dynamic evolution of COVID-19 caused by the Omicron variant via a fractional susceptible-exposed-infected-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 newly infected at the beginning of the pandemic. To characterize the specific dynamics, the Caputo-Hadamard fractional derivative is adopted to refine the classical SEIR model. Based on the reported data, we infer the fractional order and time-dependent parameters as well as unobserved dynamics of the fractional SEIR model via fractional physics-informed neural networks. Then, we make short-time predictions using the learned fractional SEIR model.
我们通过分数易感-暴露-感染-清除（SEIR）模型研究了由奥密克戎变异引起的 COVID-19 的动态演变。初步数据表明，奥密克戎感染的症状并不明显，因此传播更为隐匿，这导致在疫情初期新感染病例的检测数量增长相对缓慢。为了描述特定的动力学，我们采用 Caputo-Hadamard 分数导数来改进经典的 SEIR 模型。基于报告的数据，我们通过分数物理启发神经网络推断分数 SEIR 模型的分数阶和时变参数以及未观测到的动力学。然后，我们使用学习到的分数 SEIR 模型进行短期预测。
