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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...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer Berlin Heidelberg
2020
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| 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 |