Approach to COVID-19 time series data using deep learning and spectral analysis methods

Kayode Oshinubi; Augustina Amakor; Olumuyiwa James Peter; Mustapha Rachdi; Jacques Demongeot; Kayode Oshinubi; Augustina Amakor; Olumuyiwa James Peter; Mustapha Rachdi; Jacques Demongeot

doi:10.3934/bioeng.2022001

AIMS Bioengineering

2022, Volume 9, Issue 1: 1-21. doi: 10.3934/bioeng.2022001

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Approach to COVID-19 time series data using deep learning and spectral analysis methods

1.
Laboratory AGEIS EA 7407, Team Tools for e-Gnosis Medical, Faculty of Medicine, University Grenoble Alpes (UGA), 38700 La Tronche, France
2.
Industrial and Applied Mathematics unit, Ensimag, Grenoble INP, France
3.
Department of Mathematics, University of Ilorin, Kwara State, Nigeria

Received: 12 November 2021 Revised: 10 December 2021 Accepted: 15 December 2021 Published: 24 December 2021

This article focuses on the application of deep learning and spectral analysis to epidemiology time series data, which has recently piqued the interest of some researchers. The COVID-19 virus is still mutating, particularly the delta and omicron variants, which are known for their high level of contagiousness, but policymakers and governments are resolute in combating the pandemic's spread through a recent massive vaccination campaign of their population. We used extreme machine learning (ELM), multilayer perceptron (MLP), long short-term neural network (LSTM), gated recurrent unit (GRU), convolution neural network (CNN) and deep neural network (DNN) methods on time series data from the start of the pandemic in France, Russia, Turkey, India, United states of America (USA), Brazil and United Kingdom (UK) until September 3, 2021 to predict the daily new cases and daily deaths at different waves of the pandemic in countries considered while using root mean square error (RMSE) and relative root mean square error (rRMSE) to measure the performance of these methods. We used the spectral analysis method to convert time (days) to frequency in order to analyze the peaks of frequency and periodicity of the time series data. We also forecasted the future pandemic evolution by using ELM, MLP, and spectral analysis. Moreover, MLP achieved best performance for both daily new cases and deaths based on the evaluation metrics used. Furthermore, we discovered that errors for daily deaths are much lower than those for daily new cases. While the performance of models varies, prediction and forecasting during the period of vaccination and recent cases confirm the pandemic's prevalence level in the countries under consideration. Finally, some of the peaks observed in the time series data correspond with the proven pattern of weekly peaks that is unique to the COVID-19 time series data.
- COVID-19,
- deep learning,
- data analysis,
- spectral analysis,
- neural network
Citation: Kayode Oshinubi, Augustina Amakor, Olumuyiwa James Peter, Mustapha Rachdi, Jacques Demongeot. Approach to COVID-19 time series data using deep learning and spectral analysis methods[J]. AIMS Bioengineering, 2022, 9(1): 1-21. doi: 10.3934/bioeng.2022001

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Abstract

This article focuses on the application of deep learning and spectral analysis to epidemiology time series data, which has recently piqued the interest of some researchers. The COVID-19 virus is still mutating, particularly the delta and omicron variants, which are known for their high level of contagiousness, but policymakers and governments are resolute in combating the pandemic's spread through a recent massive vaccination campaign of their population. We used extreme machine learning (ELM), multilayer perceptron (MLP), long short-term neural network (LSTM), gated recurrent unit (GRU), convolution neural network (CNN) and deep neural network (DNN) methods on time series data from the start of the pandemic in France, Russia, Turkey, India, United states of America (USA), Brazil and United Kingdom (UK) until September 3, 2021 to predict the daily new cases and daily deaths at different waves of the pandemic in countries considered while using root mean square error (RMSE) and relative root mean square error (rRMSE) to measure the performance of these methods. We used the spectral analysis method to convert time (days) to frequency in order to analyze the peaks of frequency and periodicity of the time series data. We also forecasted the future pandemic evolution by using ELM, MLP, and spectral analysis. Moreover, MLP achieved best performance for both daily new cases and deaths based on the evaluation metrics used. Furthermore, we discovered that errors for daily deaths are much lower than those for daily new cases. While the performance of models varies, prediction and forecasting during the period of vaccination and recent cases confirm the pandemic's prevalence level in the countries under consideration. Finally, some of the peaks observed in the time series data correspond with the proven pattern of weekly peaks that is unique to the COVID-19 time series data.

Acknowledgments

The authors would like to thank the Petroleum Technology Development Fund (PTDF) Nigeria doctoral fellowship in collaboration with the Campus France Africa Unit. The authors appreciate the helpful comments and suggestions of the five anonymous reviewers, which have contributed to the overall improvement of the article.

Conflict of interest

The authors state that they have no conflicts of interest.

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