Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation

This paper proposes a data-driven approximate Bayesian computation framework for parameter estimation and uncertainty quantification of epidemic models, which incorporates two novelties: (i) the identification of the initial conditions by using plausible dynamic states that are compatible with obser...

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Detalles Bibliográficos
Autores principales: Cunha Jr, Americo, Barton, David A. W., Ritto, Thiago G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2023
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9961307/
https://www.ncbi.nlm.nih.gov/pubmed/37025428
http://dx.doi.org/10.1007/s11071-023-08327-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9961307/
https://www.ncbi.nlm.nih.gov/pubmed/37025428
http://dx.doi.org/10.1007/s11071-023-08327-8

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