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An urban-level prediction of lockdown measures impact on the prevalence of the COVID-19 pandemic
The world still suffers from the COVID-19 pandemic, which was identified in late 2019. The number of COVID-19 confirmed cases are increasing every day, and many governments are taking various measures and policies, such as city lockdown. It seriously treats people’s lives and health conditions, and...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9444099/ https://www.ncbi.nlm.nih.gov/pubmed/36090535 http://dx.doi.org/10.1186/s41118-022-00174-6 |
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author | Pourroostaei Ardakani, Saeid Xia, Tianqi Cheshmehzangi, Ali Zhang, Zhiang |
author_facet | Pourroostaei Ardakani, Saeid Xia, Tianqi Cheshmehzangi, Ali Zhang, Zhiang |
author_sort | Pourroostaei Ardakani, Saeid |
collection | PubMed |
description | The world still suffers from the COVID-19 pandemic, which was identified in late 2019. The number of COVID-19 confirmed cases are increasing every day, and many governments are taking various measures and policies, such as city lockdown. It seriously treats people’s lives and health conditions, and it is highly required to immediately take appropriate actions to minimise the virus spread and manage the COVID-19 outbreak. This paper aims to study the impact of the lockdown schedule on pandemic prevention and control in Ningbo, China. For this, machine learning techniques such as the K-nearest neighbours and Random Forest are used to predict the number of COVID-19 confirmed cases according to five scenarios, including no lockdown and 2 weeks, 1, 3, and 6 months postponed lockdown. According to the results, the random forest machine learning technique outperforms the K-nearest neighbours model in terms of mean squared error and R-square. The results support that taking an early lockdown measure minimises the number of COVID-19 confirmed cases in a city and addresses that late actions lead to a sharp COVID-19 outbreak. |
format | Online Article Text |
id | pubmed-9444099 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-94440992022-09-06 An urban-level prediction of lockdown measures impact on the prevalence of the COVID-19 pandemic Pourroostaei Ardakani, Saeid Xia, Tianqi Cheshmehzangi, Ali Zhang, Zhiang Genus Original Article The world still suffers from the COVID-19 pandemic, which was identified in late 2019. The number of COVID-19 confirmed cases are increasing every day, and many governments are taking various measures and policies, such as city lockdown. It seriously treats people’s lives and health conditions, and it is highly required to immediately take appropriate actions to minimise the virus spread and manage the COVID-19 outbreak. This paper aims to study the impact of the lockdown schedule on pandemic prevention and control in Ningbo, China. For this, machine learning techniques such as the K-nearest neighbours and Random Forest are used to predict the number of COVID-19 confirmed cases according to five scenarios, including no lockdown and 2 weeks, 1, 3, and 6 months postponed lockdown. According to the results, the random forest machine learning technique outperforms the K-nearest neighbours model in terms of mean squared error and R-square. The results support that taking an early lockdown measure minimises the number of COVID-19 confirmed cases in a city and addresses that late actions lead to a sharp COVID-19 outbreak. Springer International Publishing 2022-09-05 2022 /pmc/articles/PMC9444099/ /pubmed/36090535 http://dx.doi.org/10.1186/s41118-022-00174-6 Text en © The Author(s) 2022 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 | Original Article Pourroostaei Ardakani, Saeid Xia, Tianqi Cheshmehzangi, Ali Zhang, Zhiang An urban-level prediction of lockdown measures impact on the prevalence of the COVID-19 pandemic |
title | An urban-level prediction of lockdown measures impact on the prevalence of the COVID-19 pandemic |
title_full | An urban-level prediction of lockdown measures impact on the prevalence of the COVID-19 pandemic |
title_fullStr | An urban-level prediction of lockdown measures impact on the prevalence of the COVID-19 pandemic |
title_full_unstemmed | An urban-level prediction of lockdown measures impact on the prevalence of the COVID-19 pandemic |
title_short | An urban-level prediction of lockdown measures impact on the prevalence of the COVID-19 pandemic |
title_sort | urban-level prediction of lockdown measures impact on the prevalence of the covid-19 pandemic |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9444099/ https://www.ncbi.nlm.nih.gov/pubmed/36090535 http://dx.doi.org/10.1186/s41118-022-00174-6 |
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