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Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China
Since the outbreak of the 2019 Coronavirus disease (COVID-19) at the end of 2019, it has caused great adverse effects on the whole world, and it has been hindering the global economy. It is ergent to establish an infectious disease model for the current COVID-19 epidemic to predict the trend of the...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9133826/ https://www.ncbi.nlm.nih.gov/pubmed/35618751 http://dx.doi.org/10.1038/s41598-022-12958-z |
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author | Qiu, Zhenzhen Sun, Youyi He, Xuan Wei, Jing Zhou, Rui Bai, Jie Du, Shouying |
author_facet | Qiu, Zhenzhen Sun, Youyi He, Xuan Wei, Jing Zhou, Rui Bai, Jie Du, Shouying |
author_sort | Qiu, Zhenzhen |
collection | PubMed |
description | Since the outbreak of the 2019 Coronavirus disease (COVID-19) at the end of 2019, it has caused great adverse effects on the whole world, and it has been hindering the global economy. It is ergent to establish an infectious disease model for the current COVID-19 epidemic to predict the trend of the epidemic. Based on the SEIR model, the improved SEIR models were established with considering the incubation period, the isolated population, and genetic algorithm (GA) parameter optimization method. The improved SEIR models can predict the trend of the epidemic situation better and obtain the more accurate epidemic-related parameters. Comparing some key parameters, it is capable to evaluate the impact of different epidemic prevention measures and the implementation of different epidemic prevention levels on the COVID-19, which has significant guidance for further epidemic prevention measures. |
format | Online Article Text |
id | pubmed-9133826 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-91338262022-05-26 Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China Qiu, Zhenzhen Sun, Youyi He, Xuan Wei, Jing Zhou, Rui Bai, Jie Du, Shouying Sci Rep Article Since the outbreak of the 2019 Coronavirus disease (COVID-19) at the end of 2019, it has caused great adverse effects on the whole world, and it has been hindering the global economy. It is ergent to establish an infectious disease model for the current COVID-19 epidemic to predict the trend of the epidemic. Based on the SEIR model, the improved SEIR models were established with considering the incubation period, the isolated population, and genetic algorithm (GA) parameter optimization method. The improved SEIR models can predict the trend of the epidemic situation better and obtain the more accurate epidemic-related parameters. Comparing some key parameters, it is capable to evaluate the impact of different epidemic prevention measures and the implementation of different epidemic prevention levels on the COVID-19, which has significant guidance for further epidemic prevention measures. Nature Publishing Group UK 2022-05-26 /pmc/articles/PMC9133826/ /pubmed/35618751 http://dx.doi.org/10.1038/s41598-022-12958-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Qiu, Zhenzhen Sun, Youyi He, Xuan Wei, Jing Zhou, Rui Bai, Jie Du, Shouying Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China |
title | Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China |
title_full | Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China |
title_fullStr | Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China |
title_full_unstemmed | Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China |
title_short | Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China |
title_sort | application of genetic algorithm combined with improved seir model in predicting the epidemic trend of covid-19, china |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9133826/ https://www.ncbi.nlm.nih.gov/pubmed/35618751 http://dx.doi.org/10.1038/s41598-022-12958-z |
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