  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.
