Prediction of COVID-19 cases by multifactor driven long short-term memory (LSTM) model

Prediction of COVID-19 cases by multifactor driven long short-term memory (LSTM) model

Abstract Since December 2019, cases of COVID-19 have spread globally, caused millions of deaths and huge economic losses. To investigate the impact of different factors and predict the future trend, this study collects relevant data for 15 countries, containing 44 features in about 900 days, which c...

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Bibliographic Details
Main Authors: Yanwen Shao, Tsz Kin Wan, Kei Hang Katie Chan
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:Scientific Reports
Subjects:
COVID-19
Disease prediction
Pandemic prevention policy
LSTM
Machine learning
Online Access:https://doi.org/10.1038/s41598-025-86698-1
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https://doi.org/10.1038/s41598-025-86698-1

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