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

Shuai Han, Lukas Stelz, Horst Stoecker, Lingxiao Wang, Kai Zhou
2/17/2023
q-bio.QMcs.LGmath.DS

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Abstract

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.

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Cite this paper

@article{han2023approaching,
  title  = {Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks},
  author = {Shuai Han and Lukas Stelz and Horst Stoecker and Lingxiao Wang and Kai Zhou},
  year   = {2023},
  eprint = {2302.08796},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.08796v2}
}

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