Cargando…
Framework for enhancing the estimation of model parameters for data with a high level of uncertainty
Reliable data are essential to obtain adequate simulations for forecasting the dynamics of epidemics. In this context, several political, economic, and social factors may cause inconsistencies in the reported data, which reflect the capacity for realistic simulations and predictions. In the case of...
Autores principales: | Libotte, Gustavo B., dos Anjos, Lucas, Almeida, Regina C. C., Malta, Sandra M. C., Silva, Renato S. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8736321/ https://www.ncbi.nlm.nih.gov/pubmed/35017792 http://dx.doi.org/10.1007/s11071-021-07069-9 |
Ejemplares similares
-
Impacts of a delayed and slow-paced vaccination on cases and deaths during the COVID-19 pandemic: a modelling study
por: Barbosa Libotte, Gustavo, et al.
Publicado: (2022) -
SINDy-SA framework: enhancing nonlinear system identification with sensitivity analysis
por: Naozuka, Gustavo T., et al.
Publicado: (2022) -
Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting
por: Valeriano, João Pedro, et al.
Publicado: (2022) -
Parameter estimation and uncertainty quantification using information geometry
por: Sharp, Jesse A., et al.
Publicado: (2022) -
An automated sampling importance resampling procedure for estimating parameter uncertainty
por: Dosne, Anne-Gaëlle, et al.
Publicado: (2017)