A multivariate statistical approach to predict COVID‐19 count data with epidemiological interpretation and uncertainty quantification

For the analysis of COVID‐19 pandemic data, we propose Bayesian multinomial and Dirichlet‐multinomial autoregressive models for time‐series of counts of patients in mutually exclusive and exhaustive observational categories, defined according to the severity of the patient status and the required tr...

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Detalles Bibliográficos
Autores principales: Bartolucci, Francesco, Pennoni, Fulvia, Mira, Antonietta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8441832/
https://www.ncbi.nlm.nih.gov/pubmed/34374438
http://dx.doi.org/10.1002/sim.9129

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8441832/
https://www.ncbi.nlm.nih.gov/pubmed/34374438
http://dx.doi.org/10.1002/sim.9129

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