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Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks (preprint)
arxiv; 2023.
Preprint
in English
| PREPRINT-ARXIV | ID: ppzbmed-2302.08796v2
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.
Full text:
Available
Collection:
Preprints
Database:
PREPRINT-ARXIV
Main subject:
Brain Death
/
Communicable Diseases
/
COVID-19
Language:
English
Year:
2023
Document Type:
Preprint
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