Fractional SEIR Model and Data-Driven Predictions of COVID-19 Dynamics of Omicron Variant

TL;DR

This paper introduces a fractional SEIR model using Caputo-Hadamard derivatives and data-driven neural networks to better understand and predict the COVID-19 Omicron variant's spread, accounting for its subtle symptoms and concealed transmission.

Contribution

It develops a fractional SEIR model with neural network inference for parameters and unobserved dynamics, improving COVID-19 spread modeling and short-term predictions.

Findings

Fractional model captures slow initial case increase.

Neural networks infer time-dependent parameters.

Model provides accurate short-term forecasts.

Abstract

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.