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Geographically weighted regression model for physical, social, and economic factors affecting the COVID-19 pandemic spreading

This study aims to analyze the spatial distribution of the epidemic spread and the role of the physical, social, and economic characteristics in this spreading. A geographically weighted regression (GWR) model was built within a GIS environment using infection data monitored by the Iraqi Ministry of...

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Autores principales: Jasim, Ihsan Abbas, Fileeh, Moheb Kamil, Ebrahhem, Mustafa A., Al-Maliki, Laheab A., Al-Mamoori, Sohaib K., Al-Ansari, Nadhir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8896849/
https://www.ncbi.nlm.nih.gov/pubmed/35246792
http://dx.doi.org/10.1007/s11356-022-18564-w
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author Jasim, Ihsan Abbas
Fileeh, Moheb Kamil
Ebrahhem, Mustafa A.
Al-Maliki, Laheab A.
Al-Mamoori, Sohaib K.
Al-Ansari, Nadhir
author_facet Jasim, Ihsan Abbas
Fileeh, Moheb Kamil
Ebrahhem, Mustafa A.
Al-Maliki, Laheab A.
Al-Mamoori, Sohaib K.
Al-Ansari, Nadhir
author_sort Jasim, Ihsan Abbas
collection PubMed
description This study aims to analyze the spatial distribution of the epidemic spread and the role of the physical, social, and economic characteristics in this spreading. A geographically weighted regression (GWR) model was built within a GIS environment using infection data monitored by the Iraqi Ministry of Health records for 10 months from March to December 2020. The factors adopted in this model are the size of urban interaction areas and human gatherings, movement level and accessibility, and the volume of public services and facilities that attract people. The results show that it would be possible to deal with each administrative unit in proportion to its circumstances in light of the factors that appear in it. So, there will not be a single treatment for all areas with different urban characteristics, which sometimes helps not to stop social and economic life due to the imposition of a comprehensive ban on movement and activities. Therefore, there will be other supportive policies other than the ban, depending on the urban indicators for each region, such as reducing external movement from it or relying on preventing public activities only.
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spelling pubmed-88968492022-03-07 Geographically weighted regression model for physical, social, and economic factors affecting the COVID-19 pandemic spreading Jasim, Ihsan Abbas Fileeh, Moheb Kamil Ebrahhem, Mustafa A. Al-Maliki, Laheab A. Al-Mamoori, Sohaib K. Al-Ansari, Nadhir Environ Sci Pollut Res Int Research Article This study aims to analyze the spatial distribution of the epidemic spread and the role of the physical, social, and economic characteristics in this spreading. A geographically weighted regression (GWR) model was built within a GIS environment using infection data monitored by the Iraqi Ministry of Health records for 10 months from March to December 2020. The factors adopted in this model are the size of urban interaction areas and human gatherings, movement level and accessibility, and the volume of public services and facilities that attract people. The results show that it would be possible to deal with each administrative unit in proportion to its circumstances in light of the factors that appear in it. So, there will not be a single treatment for all areas with different urban characteristics, which sometimes helps not to stop social and economic life due to the imposition of a comprehensive ban on movement and activities. Therefore, there will be other supportive policies other than the ban, depending on the urban indicators for each region, such as reducing external movement from it or relying on preventing public activities only. Springer Berlin Heidelberg 2022-03-04 2022 /pmc/articles/PMC8896849/ /pubmed/35246792 http://dx.doi.org/10.1007/s11356-022-18564-w 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 Research Article
Jasim, Ihsan Abbas
Fileeh, Moheb Kamil
Ebrahhem, Mustafa A.
Al-Maliki, Laheab A.
Al-Mamoori, Sohaib K.
Al-Ansari, Nadhir
Geographically weighted regression model for physical, social, and economic factors affecting the COVID-19 pandemic spreading
title Geographically weighted regression model for physical, social, and economic factors affecting the COVID-19 pandemic spreading
title_full Geographically weighted regression model for physical, social, and economic factors affecting the COVID-19 pandemic spreading
title_fullStr Geographically weighted regression model for physical, social, and economic factors affecting the COVID-19 pandemic spreading
title_full_unstemmed Geographically weighted regression model for physical, social, and economic factors affecting the COVID-19 pandemic spreading
title_short Geographically weighted regression model for physical, social, and economic factors affecting the COVID-19 pandemic spreading
title_sort geographically weighted regression model for physical, social, and economic factors affecting the covid-19 pandemic spreading
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8896849/
https://www.ncbi.nlm.nih.gov/pubmed/35246792
http://dx.doi.org/10.1007/s11356-022-18564-w
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