ArticleInfectious Disease Modelling2026
Dynamic properties of an SARS-CoV-2 epidemic model via stochastic PINNs.
Article in Infectious Disease Modelling, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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