Fractional SEIR model and data-driven predictions of COVID-19 dynamics of Omicron variant
Fractional SEIR model and data-driven predictions of COVID-19 dynamics of Omicron variant
复制标题
Omicron 变体的 COVID-19 动态的分数 SEIR 模型和数据驱动预测
DOI：
10.1063/5.0099450
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发表时间：
2022-07-01
期刊：
影响因子：
2.9
通讯作者：
Li, Changpin
中科院分区：
文献类型：
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作者：
Cai, Min;Em Karniadakis, George;Li, Changpin
我们通过一个部分易感-暴露-感染-移除(SEIR)模型研究了由OMICRON变体引起的新冠肺炎的动态进化。初步数据表明，欧米克龙感染的症状不突出，因此传播更隐蔽，这导致在大流行开始时发现的新感染者病例增加相对较慢。采用Caputo-Hadamard分数阶导数对经典的SEIR模型进行精化，以刻画特定的动力学特性。基于已报道的数据，我们通过分数阶物理信息神经网络来推断分数阶和含时参数以及分数阶SEIR模型的未观测动力学。然后，我们使用学习的分数SEIR模型进行短期预测。由AIP出版公司独家授权出版。
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. Published under an exclusive license by AIP Publishing.