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Dynamics identification and forecasting of COVID-19 by switching Kalman filters

The COVID-19 pandemic has captivated scientific activity since its early days. Particular attention has been dedicated to the identification of underlying dynamics and prediction of future trend. In this work, a switching Kalman filter formalism is applied on dynamics learning and forecasting of the...

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Detalles Bibliográficos
Autores principales: Zeng, Xiaoshu, Ghanem, Roger
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7455787/
https://www.ncbi.nlm.nih.gov/pubmed/32904528
http://dx.doi.org/10.1007/s00466-020-01911-4

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