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SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD
COVID-19 is one of the biggest challenges that human beings have faced recently. Many researchers have proposed different prediction methods for establishing a virus transmission model and predicting the trend of COVID-19. Among them, the methods based on artificial intelligence are currently the mo...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Published by Elsevier Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8436575/ https://www.ncbi.nlm.nih.gov/pubmed/34563855 http://dx.doi.org/10.1016/j.compbiomed.2021.104868 |
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author | Liao, Zhifang Lan, Peng Fan, Xiaoping Kelly, Benjamin Innes, Aidan Liao, Zhining |
author_facet | Liao, Zhifang Lan, Peng Fan, Xiaoping Kelly, Benjamin Innes, Aidan Liao, Zhining |
author_sort | Liao, Zhifang |
collection | PubMed |
description | COVID-19 is one of the biggest challenges that human beings have faced recently. Many researchers have proposed different prediction methods for establishing a virus transmission model and predicting the trend of COVID-19. Among them, the methods based on artificial intelligence are currently the most interesting and widely used. However, only using artificial intelligence methods for prediction cannot capture the time change pattern of the transmission of infectious diseases. To solve this problem, this paper proposes a COVID-19 prediction model based on time-dependent SIRVD by using deep learning. This model combines deep learning technology with the mathematical model of infectious diseases, and forecasts the parameters in the mathematical model of infectious diseases by fusing deep learning models such as LSTM and other time prediction methods. In the current situation of mass vaccination, we analyzed COVID-19 data from January 15, 2021, to May 27, 2021 in seven countries – India, Argentina, Brazil, South Korea, Russia, the United Kingdom, France, Germany, and Italy. The experimental results show that the prediction model not only has a 50% improvement in single-day predictions compared to pure deep learning methods, but also can be adapted to short- and medium-term predictions, which makes the overall prediction more interpretable and robust. |
format | Online Article Text |
id | pubmed-8436575 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | The Authors. Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84365752021-09-13 SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD Liao, Zhifang Lan, Peng Fan, Xiaoping Kelly, Benjamin Innes, Aidan Liao, Zhining Comput Biol Med Article COVID-19 is one of the biggest challenges that human beings have faced recently. Many researchers have proposed different prediction methods for establishing a virus transmission model and predicting the trend of COVID-19. Among them, the methods based on artificial intelligence are currently the most interesting and widely used. However, only using artificial intelligence methods for prediction cannot capture the time change pattern of the transmission of infectious diseases. To solve this problem, this paper proposes a COVID-19 prediction model based on time-dependent SIRVD by using deep learning. This model combines deep learning technology with the mathematical model of infectious diseases, and forecasts the parameters in the mathematical model of infectious diseases by fusing deep learning models such as LSTM and other time prediction methods. In the current situation of mass vaccination, we analyzed COVID-19 data from January 15, 2021, to May 27, 2021 in seven countries – India, Argentina, Brazil, South Korea, Russia, the United Kingdom, France, Germany, and Italy. The experimental results show that the prediction model not only has a 50% improvement in single-day predictions compared to pure deep learning methods, but also can be adapted to short- and medium-term predictions, which makes the overall prediction more interpretable and robust. The Authors. Published by Elsevier Ltd. 2021-11 2021-09-13 /pmc/articles/PMC8436575/ /pubmed/34563855 http://dx.doi.org/10.1016/j.compbiomed.2021.104868 Text en © 2021 The Authors 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 Liao, Zhifang Lan, Peng Fan, Xiaoping Kelly, Benjamin Innes, Aidan Liao, Zhining SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD |
title | SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD |
title_full | SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD |
title_fullStr | SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD |
title_full_unstemmed | SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD |
title_short | SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD |
title_sort | sirvd-dl: a covid-19 deep learning prediction model based on time-dependent sirvd |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8436575/ https://www.ncbi.nlm.nih.gov/pubmed/34563855 http://dx.doi.org/10.1016/j.compbiomed.2021.104868 |
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