Data driven time-varying SEIR-LSTM/GRU algorithms to track the spread of COVID-19

DOI10.3934/mbe.2022415OpenAlexW4285176127MaRDI QIDQ2688578

Ziren Chen, Khaled M. Furati, Lin Feng, Harold A. Lay, Abdul Q. M. Khaliq

Publication date: 3 March 2023

Published in: Mathematical Biosciences and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/mbe.2022415

zbMATH Keywords

time-varying parametersdata-drivenCOVID-19SEIRLSTMGRUtime-varying reproduction number

Mathematics Subject Classification ID

Epidemiology (92D30)

Cites Work

• On the definition and the computation of the basic reproduction ratio \(R_ 0\) in models for infectious diseases in heterogeneous populations
• SEIR model with unreported infected population and dynamic parameters for the spread of COVID-19
• Compartmental Models in Epidemiology
• Identification and prediction of time-varying parameters of COVID-19 model: a data-driven deep learning approach
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