Novel cost-effective method for forecasting COVID-19 and hospital occupancy using deep learning.
COVID-19
Covid prediction
Daily covid
Hospital occupancy
LSTM
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
29 10 2024
29 10 2024
Historique:
received:
05
04
2024
accepted:
02
08
2024
medline:
30
10
2024
pubmed:
30
10
2024
entrez:
30
10
2024
Statut:
epublish
Résumé
The emergence of the COVID-19 pandemic in 2019 and its rapid global spread put healthcare systems around the world to the test. This crisis created an unprecedented level of stress in hospitals, exacerbating the already complex task of healthcare management. As a result, it led to a tragic increase in mortality rates and highlighted the urgent need for advanced predictive tools to support decision-making. To address these critical challenges, this research aims to develop and implement a predictive system capable of predicting pandemic evolution with accuracy (in terms of Mean Absolute error (MAE), Root Mean Square Error (RMSE), R
Identifiants
pubmed: 39472612
doi: 10.1038/s41598-024-69319-1
pii: 10.1038/s41598-024-69319-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
25982Subventions
Organisme : Agencia Canaria de Investigación, Innovación y Sociedad de la Información
ID : TESIS2020010118
Informations de copyright
© 2024. The Author(s).
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