Work

Fractional SEIR model and data-driven predictions of COVID-19 dynamics of Omicron variant

Year: 2022

Type: article

Abstract: 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.

Source: Chaos An Interdisciplinary Journal of Nonlinear Science

DOI: 10.1063/5.0099450

Authors: Min Cai, George Em Karniadakis, Changpin Li

Institutions: Shanghai University, Brown University

Language: English

Topic: COVID-19 epidemiological studies

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Natural Science Foundation of China