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Coronavirus disease vulnerability map using a geographic information system (GIS) from 16 April to 16 May 2020

In recent months, the world has been affected by the infectious coronavirus disease and Iran is one of the most affected countries. The Iranian government's health facilities for an urgent investigation of all provinces do not exist simultaneously. There is no management tool to identify the vu...

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Autores principales: Razavi-Termeh, Seyed Vahid, Sadeghi-Niaraki, Abolghasem, Choi, Soo-Mi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9133353/
https://www.ncbi.nlm.nih.gov/pubmed/35637755
http://dx.doi.org/10.1016/j.pce.2021.103043
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author Razavi-Termeh, Seyed Vahid
Sadeghi-Niaraki, Abolghasem
Choi, Soo-Mi
author_facet Razavi-Termeh, Seyed Vahid
Sadeghi-Niaraki, Abolghasem
Choi, Soo-Mi
author_sort Razavi-Termeh, Seyed Vahid
collection PubMed
description In recent months, the world has been affected by the infectious coronavirus disease and Iran is one of the most affected countries. The Iranian government's health facilities for an urgent investigation of all provinces do not exist simultaneously. There is no management tool to identify the vulnerabilities of Iranian provinces in prioritizing health services. The aim of this study was to prepare a coronavirus vulnerability map of Iranian provinces using geographic information system (GIS) to monitor the disease. For this purpose, four criteria affecting coronavirus, including population density, percentage of older people, temperature, and humidity, were prepared in the GIS. A multiscale geographically weighted regression (MGWR) model was used to determine the vulnerability of coronavirus in Iran. An adaptive neuro-fuzzy inference system (ANFIS) model was used to predict vulnerability in the next two months. Results indicated that, population density and older people have a more significant impact on coronavirus in Iran. Based on MGWR models, Tehran, Mazandaran, Gilan, and Alborz provinces were more vulnerable to coronavirus in February and March. The ANFIS model findings showed that West Azerbaijan, Zanjan, Fars, Yazd, Semnan, Sistan and Baluchistan, and Tehran provinces were more vulnerable in April and May.
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spelling pubmed-91333532022-05-26 Coronavirus disease vulnerability map using a geographic information system (GIS) from 16 April to 16 May 2020 Razavi-Termeh, Seyed Vahid Sadeghi-Niaraki, Abolghasem Choi, Soo-Mi Phys Chem Earth (2002) Article In recent months, the world has been affected by the infectious coronavirus disease and Iran is one of the most affected countries. The Iranian government's health facilities for an urgent investigation of all provinces do not exist simultaneously. There is no management tool to identify the vulnerabilities of Iranian provinces in prioritizing health services. The aim of this study was to prepare a coronavirus vulnerability map of Iranian provinces using geographic information system (GIS) to monitor the disease. For this purpose, four criteria affecting coronavirus, including population density, percentage of older people, temperature, and humidity, were prepared in the GIS. A multiscale geographically weighted regression (MGWR) model was used to determine the vulnerability of coronavirus in Iran. An adaptive neuro-fuzzy inference system (ANFIS) model was used to predict vulnerability in the next two months. Results indicated that, population density and older people have a more significant impact on coronavirus in Iran. Based on MGWR models, Tehran, Mazandaran, Gilan, and Alborz provinces were more vulnerable to coronavirus in February and March. The ANFIS model findings showed that West Azerbaijan, Zanjan, Fars, Yazd, Semnan, Sistan and Baluchistan, and Tehran provinces were more vulnerable in April and May. Elsevier Ltd. 2022-06 2021-06-16 /pmc/articles/PMC9133353/ /pubmed/35637755 http://dx.doi.org/10.1016/j.pce.2021.103043 Text en © 2021 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Razavi-Termeh, Seyed Vahid
Sadeghi-Niaraki, Abolghasem
Choi, Soo-Mi
Coronavirus disease vulnerability map using a geographic information system (GIS) from 16 April to 16 May 2020
title Coronavirus disease vulnerability map using a geographic information system (GIS) from 16 April to 16 May 2020
title_full Coronavirus disease vulnerability map using a geographic information system (GIS) from 16 April to 16 May 2020
title_fullStr Coronavirus disease vulnerability map using a geographic information system (GIS) from 16 April to 16 May 2020
title_full_unstemmed Coronavirus disease vulnerability map using a geographic information system (GIS) from 16 April to 16 May 2020
title_short Coronavirus disease vulnerability map using a geographic information system (GIS) from 16 April to 16 May 2020
title_sort coronavirus disease vulnerability map using a geographic information system (gis) from 16 april to 16 may 2020
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9133353/
https://www.ncbi.nlm.nih.gov/pubmed/35637755
http://dx.doi.org/10.1016/j.pce.2021.103043
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