Work

Dynamic properties of an SARS-CoV-2 epidemic model via stochastic PINNs

Year: 2026

Type: article

Abstract: This paper introduces a novel stochastic SEIRV model for investigating the spread of the SARS-CoV-2 epidemic using stochastic physics-informed neural networks (S-PINNs). We first prove the global positivity of solutions via Lyapunov functions and Itô's formula. Then, persistence and extinction properties are analyzed by a threshold. The S-PINNs algorithm integrates noise into the loss function for seamless data-driven and physics-based learning. Finally, the algorithm based on the SEIRV model is applied to SARS-CoV-2 data from Austria, Switzerland, and Belgium, and it outperforms PINNs, LSTMs, and Logistic models, especially on noisy data.

Source: Infectious Disease Modelling

DOI: 10.1016/j.idm.2026.03.012

Authors: Linfeng Xie, Jikai Yang, Zhiming Li

Institution: Xinjiang University

Language: English

Topic: Mathematical and Theoretical Epidemiology and Ecology Models

Subfield: Public Health, Environmental and Occupational Health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,100