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Transfer-recursive-ensemble learning for multi-day COVID-19 prediction in India using recurrent neural networks
The COVID-19 pandemic has put a huge challenge on the Indian health infrastructure. With a larger number of people getting affected during the second wave, hospitals were overburdened, running out of supplies and oxygen. Hence, predicting new COVID-19 cases, new deaths, and total active cases multip...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10130813/ https://www.ncbi.nlm.nih.gov/pubmed/37100806 http://dx.doi.org/10.1038/s41598-023-31737-y |
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author | Chakraborty, Debasrita Goswami, Debayan Ghosh, Susmita Ghosh, Ashish Chan, Jonathan H. Wang, Lipo |
author_facet | Chakraborty, Debasrita Goswami, Debayan Ghosh, Susmita Ghosh, Ashish Chan, Jonathan H. Wang, Lipo |
author_sort | Chakraborty, Debasrita |
collection | PubMed |
description | The COVID-19 pandemic has put a huge challenge on the Indian health infrastructure. With a larger number of people getting affected during the second wave, hospitals were overburdened, running out of supplies and oxygen. Hence, predicting new COVID-19 cases, new deaths, and total active cases multiple days in advance can aid better utilization of scarce medical resources and prudent pandemic-related decision-making. The proposed method uses gated recurrent unit networks as the main predicting model. A study is conducted by building four models pre-trained on COVID-19 data from four different countries (United States of America, Brazil, Spain, and Bangladesh) and fine-tuned on India’s data. Since the four countries chosen have experienced different types of infection curves, the pre-training provides a transfer learning to the models incorporating diverse situations into account. Each of the four models then gives 7-day ahead predictions using the recursive learning method for the Indian test data. The final prediction comes from an ensemble of the predictions of the different models. This method with two countries, Spain and Bangladesh, is seen to achieve the best performance amongst all the combinations as well as compared to other traditional regression models. |
format | Online Article Text |
id | pubmed-10130813 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101308132023-04-27 Transfer-recursive-ensemble learning for multi-day COVID-19 prediction in India using recurrent neural networks Chakraborty, Debasrita Goswami, Debayan Ghosh, Susmita Ghosh, Ashish Chan, Jonathan H. Wang, Lipo Sci Rep Article The COVID-19 pandemic has put a huge challenge on the Indian health infrastructure. With a larger number of people getting affected during the second wave, hospitals were overburdened, running out of supplies and oxygen. Hence, predicting new COVID-19 cases, new deaths, and total active cases multiple days in advance can aid better utilization of scarce medical resources and prudent pandemic-related decision-making. The proposed method uses gated recurrent unit networks as the main predicting model. A study is conducted by building four models pre-trained on COVID-19 data from four different countries (United States of America, Brazil, Spain, and Bangladesh) and fine-tuned on India’s data. Since the four countries chosen have experienced different types of infection curves, the pre-training provides a transfer learning to the models incorporating diverse situations into account. Each of the four models then gives 7-day ahead predictions using the recursive learning method for the Indian test data. The final prediction comes from an ensemble of the predictions of the different models. This method with two countries, Spain and Bangladesh, is seen to achieve the best performance amongst all the combinations as well as compared to other traditional regression models. Nature Publishing Group UK 2023-04-26 /pmc/articles/PMC10130813/ /pubmed/37100806 http://dx.doi.org/10.1038/s41598-023-31737-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Chakraborty, Debasrita Goswami, Debayan Ghosh, Susmita Ghosh, Ashish Chan, Jonathan H. Wang, Lipo Transfer-recursive-ensemble learning for multi-day COVID-19 prediction in India using recurrent neural networks |
title | Transfer-recursive-ensemble learning for multi-day COVID-19 prediction in India using recurrent neural networks |
title_full | Transfer-recursive-ensemble learning for multi-day COVID-19 prediction in India using recurrent neural networks |
title_fullStr | Transfer-recursive-ensemble learning for multi-day COVID-19 prediction in India using recurrent neural networks |
title_full_unstemmed | Transfer-recursive-ensemble learning for multi-day COVID-19 prediction in India using recurrent neural networks |
title_short | Transfer-recursive-ensemble learning for multi-day COVID-19 prediction in India using recurrent neural networks |
title_sort | transfer-recursive-ensemble learning for multi-day covid-19 prediction in india using recurrent neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10130813/ https://www.ncbi.nlm.nih.gov/pubmed/37100806 http://dx.doi.org/10.1038/s41598-023-31737-y |
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