Data-driven optimal control of a SEIR model for COVID-19
We present a data-driven optimal control approach which integrates the reported partial data with the epidemic dynamics for COVID-19. We use a basic Susceptible-Exposed-Infectious-Recovered (SEIR) model, the model parameters are time-varying and learned from the data. This approach serves to forecast the evolution of the outbreak over a relatively short time period and provide scheduled controls of the epidemic. We provide efficient numerical algorithms based on a generalized Pontryagin Maximum Principle associated with the optimal control theory. Numerical experiments demonstrate the effective performance of the proposed model and its numerical approximations..
Medienart: |
Preprint |
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Erscheinungsjahr: |
2020 |
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Erschienen: |
2020 |
Enthalten in: |
arXiv.org - (2020) vom: 01. Dez. Zur Gesamtaufnahme - year:2020 |
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Sprache: |
Englisch |
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Beteiligte Personen: |
Liu, Hailiang [VerfasserIn] |
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Links: |
Volltext [kostenfrei] |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
XAR019457723 |
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