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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...

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Autores principales: Pourroostaei Ardakani, Saeid, Xia, Tianqi, Cheshmehzangi, Ali, Zhang, Zhiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
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.
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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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