Identification and prediction of time-varying parameters of COVID-19 model: a data-driven deep learning approach

DOI10.1080/00207160.2021.1929942zbMath1483.92138arXiv2103.09949OpenAlexW3162244563MaRDI QIDQ5031306

Jie Long, Abdul Q. M. Khaliq, Khaled M. Furati

Publication date: 18 February 2022

Published in: International Journal of Computer Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2103.09949

zbMATH Keywords

deep neural networkCOVID-19LSTMPINNSIRD

Mathematics Subject Classification ID

Epidemiology (92D30) Artificial neural networks and deep learning (68T07)

Related Items (3)

SEIR model with unreported infected population and dynamic parameters for the spread of COVID-19 ⋮ Reduced modelling and optimal control of epidemiological individual‐based models with contact heterogeneity ⋮ Data driven time-varying SEIR-LSTM/GRU algorithms to track the spread of COVID-19

Uses Software

• Adam
• DeepXDE

Cites Work

• Backpropagation and stochastic gradient descent method
• Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data
• Memory-based meso-scale modeling of Covid-19
• Design of a nonlinear model for the propagation of COVID-19 and its efficient nonstandard computational implementation
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• SARS epidemical forecast research in mathematical model
• DeepXDE: A Deep Learning Library for Solving Differential Equations
• On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs
• Approximation by superpositions of a sigmoidal function
• Unnamed Item

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