Bayesian sequential data assimilation for COVID-19 forecasting

We introduce a Bayesian sequential data assimilation and forecasting method for non-autonomous dynamical systems. We applied this method to the current COVID-19 pandemic. It is assumed that suitable transmission, epidemic and observation models are available and previously validated. The transmissio...

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Detalles Bibliográficos
Autores principales: Daza-Torres, Maria L., Capistrán, Marcos A., Capella, Antonio, Christen, J. Andrés
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier B.V. 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9023479/
https://www.ncbi.nlm.nih.gov/pubmed/35487155
http://dx.doi.org/10.1016/j.epidem.2022.100564

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9023479/
https://www.ncbi.nlm.nih.gov/pubmed/35487155
http://dx.doi.org/10.1016/j.epidem.2022.100564

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