Hybrid modeling approaches for predicting COVID-19 mortality: A comparative study across USA, France, and India

The precise forecasting of COVID-19 mortality is essential for prompt public health measures and resource distribution. This paper proposes and assesses three Gaussian Process-based hybrid models—GP-RBM (Random Boosting Machine), GP-LSTM (Long Short-Term Memory), and GP-CNN (Convolutional Neural Net...

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Autors principals:	B. Uppalaiah, D. Mallikarjuna Reddy, K. Rajalakshmi, P. Vignesh, V. Govindan, Siriluk Donganont
Format:	Artigo
Idioma:	Inglês
Publicat:	Elsevier 2025-06-01
Col·lecció:	Results in Engineering
Matèries:	COVID-19 mortality prediction Hybrid machine learning models Gaussian process (GP) Model evaluation metrics Epidemiological forecasting
Accés en línia:	http://www.sciencedirect.com/science/article/pii/S2590123025011673
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