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

Lin Feng; Ziren Chen; Harold A. Lay Jr.; Khaled Furati; Abdul Khaliq; Lin Feng; Ziren Chen; Harold A. Lay Jr.; Khaled Furati; Abdul Khaliq

doi:10.3934/mbe.2022415

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 9: 8935-8962. doi: 10.3934/mbe.2022415

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Research article Special Issues

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

1.
Department of Mathematics, Iowa State University, Ames, IA 50011, USA
2.
Thompson Machinery Commerce Corporation, 1245 Bridgestone Blvd LaVergne, TN 37086, USA
3.
Department of Mathematics, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
4.
Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, TN 37132, USA

Academic Editor: Zhuolin Qu

Received: 06 January 2022 Revised: 01 March 2022 Accepted: 21 March 2022 Published: 20 June 2022

COVID-19 is an infectious disease caused by a newly discovered coronavirus, which has become a worldwide pandemic greatly impacting our daily life and work. A large number of mathematical models, including the susceptible-exposed-infected-removed (SEIR) model and deep learning methods, such as long-short-term-memory (LSTM) and gated recurrent units (GRU)-based methods, have been employed for the analysis and prediction of the COVID-19 outbreak. This paper describes a SEIR-LSTM/GRU algorithm with time-varying parameters that can predict the number of active cases and removed cases in the US. Time-varying reproductive numbers that can illustrate the progress of the epidemic are also produced via this process. The investigation is based on the active cases and total cases data for the USA, as collected from the website "Worldometer". The root mean square error, mean absolute percentage error and $ r_2 $ score were utilized to assess the model's accuracy.
- SEIR,
- LSTM,
- GRU,
- time-varying parameters,
- data-driven,
- COVID-19,
- time-varying reproduction number
Citation: Lin Feng, Ziren Chen, Harold A. Lay Jr., Khaled Furati, Abdul Khaliq. Data driven time-varying SEIR-LSTM/GRU algorithms to track the spread of COVID-19[J]. Mathematical Biosciences and Engineering, 2022, 19(9): 8935-8962. doi: 10.3934/mbe.2022415

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Abstract

COVID-19 is an infectious disease caused by a newly discovered coronavirus, which has become a worldwide pandemic greatly impacting our daily life and work. A large number of mathematical models, including the susceptible-exposed-infected-removed (SEIR) model and deep learning methods, such as long-short-term-memory (LSTM) and gated recurrent units (GRU)-based methods, have been employed for the analysis and prediction of the COVID-19 outbreak. This paper describes a SEIR-LSTM/GRU algorithm with time-varying parameters that can predict the number of active cases and removed cases in the US. Time-varying reproductive numbers that can illustrate the progress of the epidemic are also produced via this process. The investigation is based on the active cases and total cases data for the USA, as collected from the website "Worldometer". The root mean square error, mean absolute percentage error and $ r_2 $ score were utilized to assess the model's accuracy.

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