A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models

This study aims to develop an assumption-free data-driven model to accurately forecast COVID-19 spread. Towards this end, we firstly employed Bayesian optimization to tune the Gaussian process regression (GPR) hyperparameters to develop an efficient GPR-based model for forecasting the recovered and...

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Detalles Bibliográficos
Autores principales: Alali, Yasminah, Harrou, Fouzi, Sun, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8844088/
https://www.ncbi.nlm.nih.gov/pubmed/35165290
http://dx.doi.org/10.1038/s41598-022-06218-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8844088/
https://www.ncbi.nlm.nih.gov/pubmed/35165290
http://dx.doi.org/10.1038/s41598-022-06218-3

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