mlsanityData to 7 Oct 2026

Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks

arXiv 2302.08796 ↗First posted 17 Feb 2023q-bio.QM cs.LG math.DScited by 7

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Abstract (arXiv)

A physics-informed neural network (PINN) embedded with the susceptible-infected-removed (SIR) model is devised to understand the temporal evolution dynamics of infectious diseases. Firstly, the effectiveness of this approach is demonstrated on synthetic data as generated from the numerical solution of the susceptible-asymptomatic-infected-recovered-dead (SAIRD) model. Then, the method is applied to COVID-19 data reported for Germany and shows that it can accurately identify and predict virus spread trends. The results indicate that an incomplete physics-informed model can approach more complicated dynamics efficiently. Thus, the present work demonstrates the high potential of using machine learning methods, e.g., PINNs, to study and predict epidemic dynamics in combination with compartmental models.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 10 of its 57 references that are in the map. First posted on arXiv 17 Feb 2023.

Builds on

10 of 57 references.

References · 10

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  3. 2022Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systemscited by 54
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Cited by year

Citing papers per year: 2025: 7.

7 papers.

  1. 2025Identifying Memory Effects in Epidemics via a Fractional SEIRD Model and Physics-Informed Neural Networkscited by 2
  2. 2025A data augmentation strategy for deep neural networks with application to epidemic modellingcited by 1
  3. 2025Augmented data and neural networks for robust epidemic forecasting: application to COVID-19 in Italycited by 1
  4. 2025Exploration of Hepatitis B Virus Infection Dynamics through Physics-Informed Deep Learning Approachcited by 0
  5. 2025Online Cluster-Based Parameter Control for Metaheuristiccited by 0
  6. 2025A Physics-Informed Neural Networks-Based Model Predictive Control Framework for SIR Epidemicscited by 0
  7. 2025Modeling COVID-19 Dynamics in German States Using Physics-Informed Neural Networkscited by 0