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Prediction of highly vulnerable areas to COVID-19 outbreaks using spatial model: Case study of Cairo Governorate, Egypt

COVID-19 has affected over 170 countries around the world. Alarming rate has increased with the increase of infected cases and death rates. Whereas, the World Health Organization (WHO) had declared the COVID-19 virus as a pandemic on 11th March 2020. Preparations were made to face the spread of COVI...

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Autores principales: Ramadan, Rasha H., Ramadan, Mona S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8352670/
http://dx.doi.org/10.1016/j.ejrs.2021.08.003
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author Ramadan, Rasha H.
Ramadan, Mona S.
author_facet Ramadan, Rasha H.
Ramadan, Mona S.
author_sort Ramadan, Rasha H.
collection PubMed
description COVID-19 has affected over 170 countries around the world. Alarming rate has increased with the increase of infected cases and death rates. Whereas, the World Health Organization (WHO) had declared the COVID-19 virus as a pandemic on 11th March 2020. Preparations were made to face the spread of COVID-19, as predicting the most probable risk areas by using spatial models. Prediction spatial models of COVID-19 risk areas can help the governmental authorities to generate sustainable strategies and set up suitable protocols to control the pandemic. This research presents an attempt of a potential spatial prediction modeling of COVID-19 risk areas in Cairo governorate-Egypt. Four indicator models (demographic, residential, environmental and topographic) were developed using geomatics technology based on the guidelines of the UN-habitat sustainable development goals (SDGs) target (11 & 3). Five predicted scenarios were generated for the most pandemic probability areas by the integration of the four indicator models. The results showed that there are common areas in all scenarios for highly COVID-19 pandemic risk areas. These common risk areas were found in (El Marag, El Salam, Ain Shams, El Mataria, El Gammaleya, Manshiat Nasser, El Mosky, Bolak, Hadaak El Koba, and El Sharbeya) districts. The hotspots zones are characterized by overcrowding, high population density and economic activities, large family size, poor infrastructure service and low rate of education. Moreover, it was noticed that crowding points resulted in traffic density and air pollution, which may affect the pandemic spread. The accuracy assessment results displayed that, the environmental predicted scenario was more consistent with the official data of the Egyptian Ministry of Health and Population) MOHP), while the residential one was less convenient. The result of this study supports the health sector by predicting the hot spots areas. The present study is aimed to develop a proactive plan to confront the pandemic before spreading in the Cairo governorate-Egypt. Also, the proposed prediction model can be an effective aid for decision-makers across the world working on containment strategies to minimize the spread of Coronavirus.
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spelling pubmed-83526702021-08-10 Prediction of highly vulnerable areas to COVID-19 outbreaks using spatial model: Case study of Cairo Governorate, Egypt Ramadan, Rasha H. Ramadan, Mona S. The Egyptian Journal of Remote Sensing and Space Sciences Research Paper COVID-19 has affected over 170 countries around the world. Alarming rate has increased with the increase of infected cases and death rates. Whereas, the World Health Organization (WHO) had declared the COVID-19 virus as a pandemic on 11th March 2020. Preparations were made to face the spread of COVID-19, as predicting the most probable risk areas by using spatial models. Prediction spatial models of COVID-19 risk areas can help the governmental authorities to generate sustainable strategies and set up suitable protocols to control the pandemic. This research presents an attempt of a potential spatial prediction modeling of COVID-19 risk areas in Cairo governorate-Egypt. Four indicator models (demographic, residential, environmental and topographic) were developed using geomatics technology based on the guidelines of the UN-habitat sustainable development goals (SDGs) target (11 & 3). Five predicted scenarios were generated for the most pandemic probability areas by the integration of the four indicator models. The results showed that there are common areas in all scenarios for highly COVID-19 pandemic risk areas. These common risk areas were found in (El Marag, El Salam, Ain Shams, El Mataria, El Gammaleya, Manshiat Nasser, El Mosky, Bolak, Hadaak El Koba, and El Sharbeya) districts. The hotspots zones are characterized by overcrowding, high population density and economic activities, large family size, poor infrastructure service and low rate of education. Moreover, it was noticed that crowding points resulted in traffic density and air pollution, which may affect the pandemic spread. The accuracy assessment results displayed that, the environmental predicted scenario was more consistent with the official data of the Egyptian Ministry of Health and Population) MOHP), while the residential one was less convenient. The result of this study supports the health sector by predicting the hot spots areas. The present study is aimed to develop a proactive plan to confront the pandemic before spreading in the Cairo governorate-Egypt. Also, the proposed prediction model can be an effective aid for decision-makers across the world working on containment strategies to minimize the spread of Coronavirus. National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. 2022-02 2021-08-10 /pmc/articles/PMC8352670/ http://dx.doi.org/10.1016/j.ejrs.2021.08.003 Text en © 2021 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. 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 Research Paper
Ramadan, Rasha H.
Ramadan, Mona S.
Prediction of highly vulnerable areas to COVID-19 outbreaks using spatial model: Case study of Cairo Governorate, Egypt
title Prediction of highly vulnerable areas to COVID-19 outbreaks using spatial model: Case study of Cairo Governorate, Egypt
title_full Prediction of highly vulnerable areas to COVID-19 outbreaks using spatial model: Case study of Cairo Governorate, Egypt
title_fullStr Prediction of highly vulnerable areas to COVID-19 outbreaks using spatial model: Case study of Cairo Governorate, Egypt
title_full_unstemmed Prediction of highly vulnerable areas to COVID-19 outbreaks using spatial model: Case study of Cairo Governorate, Egypt
title_short Prediction of highly vulnerable areas to COVID-19 outbreaks using spatial model: Case study of Cairo Governorate, Egypt
title_sort prediction of highly vulnerable areas to covid-19 outbreaks using spatial model: case study of cairo governorate, egypt
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8352670/
http://dx.doi.org/10.1016/j.ejrs.2021.08.003
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