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Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization
COVID-19 virus has encountered people in the world with numerous problems. Given the negative impacts of COVID-19 on all aspects of people's lives, especially health and economy, accurately forecasting the number of cases infected with this virus can help governments to make accurate decisions...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7699029/ https://www.ncbi.nlm.nih.gov/pubmed/33281305 http://dx.doi.org/10.1016/j.chaos.2020.110511 |
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author | Abbasimehr, Hossein Paki, Reza |
author_facet | Abbasimehr, Hossein Paki, Reza |
author_sort | Abbasimehr, Hossein |
collection | PubMed |
description | COVID-19 virus has encountered people in the world with numerous problems. Given the negative impacts of COVID-19 on all aspects of people's lives, especially health and economy, accurately forecasting the number of cases infected with this virus can help governments to make accurate decisions on the interventions that must be taken. In this study, we propose three hybrid approaches for forecasting COVID-19 time series methods based on combining three deep learning models such as multi-head attention, long short-term memory (LSTM), and convolutional neural network (CNN) with the Bayesian optimization algorithm. All models are designed based on the multiple-output forecasting strategy, which allows the forecasting of the multiple time points. The Bayesian optimization method automatically selects the best hyperparameters for each model and enhances forecasting performance. Using the publicly available epidemical data acquired from Johns Hopkins University's Coronavirus Resource Center, we conducted our experiments and evaluated the proposed models against the benchmark model. The results of experiments exhibit the superiority of the deep learning models over the benchmark model both for short-term forecasting and long-horizon forecasting. In particular, the mean SMAPE of the best deep learning model is 0.25 for the short-term forecasting (10 days ahead). Also, for long-horizon forecasting, the best deep learning model obtains the mean SMAPE of 2.59. |
format | Online Article Text |
id | pubmed-7699029 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-76990292020-12-01 Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization Abbasimehr, Hossein Paki, Reza Chaos Solitons Fractals Article COVID-19 virus has encountered people in the world with numerous problems. Given the negative impacts of COVID-19 on all aspects of people's lives, especially health and economy, accurately forecasting the number of cases infected with this virus can help governments to make accurate decisions on the interventions that must be taken. In this study, we propose three hybrid approaches for forecasting COVID-19 time series methods based on combining three deep learning models such as multi-head attention, long short-term memory (LSTM), and convolutional neural network (CNN) with the Bayesian optimization algorithm. All models are designed based on the multiple-output forecasting strategy, which allows the forecasting of the multiple time points. The Bayesian optimization method automatically selects the best hyperparameters for each model and enhances forecasting performance. Using the publicly available epidemical data acquired from Johns Hopkins University's Coronavirus Resource Center, we conducted our experiments and evaluated the proposed models against the benchmark model. The results of experiments exhibit the superiority of the deep learning models over the benchmark model both for short-term forecasting and long-horizon forecasting. In particular, the mean SMAPE of the best deep learning model is 0.25 for the short-term forecasting (10 days ahead). Also, for long-horizon forecasting, the best deep learning model obtains the mean SMAPE of 2.59. Elsevier Ltd. 2021-01 2020-11-28 /pmc/articles/PMC7699029/ /pubmed/33281305 http://dx.doi.org/10.1016/j.chaos.2020.110511 Text en © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Abbasimehr, Hossein Paki, Reza Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization |
title | Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization |
title_full | Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization |
title_fullStr | Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization |
title_full_unstemmed | Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization |
title_short | Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization |
title_sort | prediction of covid-19 confirmed cases combining deep learning methods and bayesian optimization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7699029/ https://www.ncbi.nlm.nih.gov/pubmed/33281305 http://dx.doi.org/10.1016/j.chaos.2020.110511 |
work_keys_str_mv | AT abbasimehrhossein predictionofcovid19confirmedcasescombiningdeeplearningmethodsandbayesianoptimization AT pakireza predictionofcovid19confirmedcasescombiningdeeplearningmethodsandbayesianoptimization |