Evaluation of Vulnerability Status of the Infection Risk to COVID-19 Using Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA): A Case Study of Addis Ababa City, Ethiopia
COVID-19 is a disease caused by a new coronavirus called SARS-CoV-2 and is an accidental global public health threat. Because of this, WHO declared the COVID-19 outbreak a pandemic. The pandemic is spreading unprecedently in Addis Ababa, which results in extraordinary logistical and management chall...
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container_title | International journal of environmental research and public health |
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creator | Asfaw, Hizkel Karuppannan, Shankar Erduno, Tilahun Almohamad, Hussein Dughairi, Ahmed Abdullah Al Al-Mutiry, Motrih Abdo, Hazem Ghassan |
description | COVID-19 is a disease caused by a new coronavirus called SARS-CoV-2 and is an accidental global public health threat. Because of this, WHO declared the COVID-19 outbreak a pandemic. The pandemic is spreading unprecedently in Addis Ababa, which results in extraordinary logistical and management challenges in response to the novel coronavirus in the city. Thus, management strategies and resource allocation need to be vulnerability-oriented. Though various studies have been carried out on COVID-19, only a few studies have been conducted on vulnerability from a geospatial/location-based perspective but at a wider spatial resolution. This puts the results of those studies under question while their findings are projected to the finer spatial resolution. To overcome such problems, the integration of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) has been developed as a framework to evaluate and map the susceptibility status of the infection risk to COVID-19. To achieve the objective of the study, data like land use, population density, and distance from roads, hospitals, bus stations, the bank, markets, COVID-19 cases, health care units, and government offices are used. The weighted overlay method was used; to evaluate and map the susceptibility status of the infection risk to COVID-19. The result revealed that out of the total study area, 32.62% (169.91 km
) falls under the low vulnerable category (1), and the area covering 40.9% (213.04 km
) under the moderate vulnerable class (2) for infection risk of COVID-19. The highly vulnerable category (3) covers an area of 25.31% (132.85 km
), and the remaining 1.17% (6.12 km
) is under an extremely high vulnerable class (4). Thus, these priority areas could address pandemic control mechanisms like disinfection regularly. Health sector professionals, local authorities, the scientific community, and the general public will benefit from the study as a tool to better understand pandemic transmission centers and identify areas where more protective measures and response actions are needed at a finer spatial resolution. |
doi_str_mv | 10.3390/ijerph19137811 |
format | Article |
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) falls under the low vulnerable category (1), and the area covering 40.9% (213.04 km
) under the moderate vulnerable class (2) for infection risk of COVID-19. The highly vulnerable category (3) covers an area of 25.31% (132.85 km
), and the remaining 1.17% (6.12 km
) is under an extremely high vulnerable class (4). Thus, these priority areas could address pandemic control mechanisms like disinfection regularly. Health sector professionals, local authorities, the scientific community, and the general public will benefit from the study as a tool to better understand pandemic transmission centers and identify areas where more protective measures and response actions are needed at a finer spatial resolution.</description><identifier>ISSN: 1660-4601</identifier><identifier>ISSN: 1661-7827</identifier><identifier>EISSN: 1660-4601</identifier><identifier>DOI: 10.3390/ijerph19137811</identifier><identifier>PMID: 35805472</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Coronaviruses ; COVID-19 ; COVID-19 - epidemiology ; Decision making ; Decision Support Techniques ; Disease Susceptibility ; Disinfection ; Ethiopia - epidemiology ; Evaluation ; Geographic Information Systems ; Health care ; Health risks ; Humans ; Infections ; Land use ; Multiple criterion ; Population density ; Public health ; Resource allocation ; Risk ; SARS-CoV-2 ; Severe acute respiratory syndrome coronavirus 2 ; Spatial discrimination ; Spatial resolution</subject><ispartof>International journal of environmental research and public health, 2022-06, Vol.19 (13), p.7811</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 by the authors. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c418t-d7b6fb1286fba732b4142f7a29996cf845c57e55f6b0161f9cdd3e6ddd833e153</citedby><cites>FETCH-LOGICAL-c418t-d7b6fb1286fba732b4142f7a29996cf845c57e55f6b0161f9cdd3e6ddd833e153</cites><orcidid>0000-0002-1497-8611 ; 0000-0001-5014-7885 ; 0000-0001-9283-3947 ; 0000-0001-8887-915X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9266098/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9266098/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35805472$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Asfaw, Hizkel</creatorcontrib><creatorcontrib>Karuppannan, Shankar</creatorcontrib><creatorcontrib>Erduno, Tilahun</creatorcontrib><creatorcontrib>Almohamad, Hussein</creatorcontrib><creatorcontrib>Dughairi, Ahmed Abdullah Al</creatorcontrib><creatorcontrib>Al-Mutiry, Motrih</creatorcontrib><creatorcontrib>Abdo, Hazem Ghassan</creatorcontrib><title>Evaluation of Vulnerability Status of the Infection Risk to COVID-19 Using Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA): A Case Study of Addis Ababa City, Ethiopia</title><title>International journal of environmental research and public health</title><addtitle>Int J Environ Res Public Health</addtitle><description>COVID-19 is a disease caused by a new coronavirus called SARS-CoV-2 and is an accidental global public health threat. Because of this, WHO declared the COVID-19 outbreak a pandemic. The pandemic is spreading unprecedently in Addis Ababa, which results in extraordinary logistical and management challenges in response to the novel coronavirus in the city. Thus, management strategies and resource allocation need to be vulnerability-oriented. Though various studies have been carried out on COVID-19, only a few studies have been conducted on vulnerability from a geospatial/location-based perspective but at a wider spatial resolution. This puts the results of those studies under question while their findings are projected to the finer spatial resolution. To overcome such problems, the integration of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) has been developed as a framework to evaluate and map the susceptibility status of the infection risk to COVID-19. To achieve the objective of the study, data like land use, population density, and distance from roads, hospitals, bus stations, the bank, markets, COVID-19 cases, health care units, and government offices are used. The weighted overlay method was used; to evaluate and map the susceptibility status of the infection risk to COVID-19. The result revealed that out of the total study area, 32.62% (169.91 km
) falls under the low vulnerable category (1), and the area covering 40.9% (213.04 km
) under the moderate vulnerable class (2) for infection risk of COVID-19. The highly vulnerable category (3) covers an area of 25.31% (132.85 km
), and the remaining 1.17% (6.12 km
) is under an extremely high vulnerable class (4). Thus, these priority areas could address pandemic control mechanisms like disinfection regularly. 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Because of this, WHO declared the COVID-19 outbreak a pandemic. The pandemic is spreading unprecedently in Addis Ababa, which results in extraordinary logistical and management challenges in response to the novel coronavirus in the city. Thus, management strategies and resource allocation need to be vulnerability-oriented. Though various studies have been carried out on COVID-19, only a few studies have been conducted on vulnerability from a geospatial/location-based perspective but at a wider spatial resolution. This puts the results of those studies under question while their findings are projected to the finer spatial resolution. To overcome such problems, the integration of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) has been developed as a framework to evaluate and map the susceptibility status of the infection risk to COVID-19. To achieve the objective of the study, data like land use, population density, and distance from roads, hospitals, bus stations, the bank, markets, COVID-19 cases, health care units, and government offices are used. The weighted overlay method was used; to evaluate and map the susceptibility status of the infection risk to COVID-19. The result revealed that out of the total study area, 32.62% (169.91 km
) falls under the low vulnerable category (1), and the area covering 40.9% (213.04 km
) under the moderate vulnerable class (2) for infection risk of COVID-19. The highly vulnerable category (3) covers an area of 25.31% (132.85 km
), and the remaining 1.17% (6.12 km
) is under an extremely high vulnerable class (4). Thus, these priority areas could address pandemic control mechanisms like disinfection regularly. Health sector professionals, local authorities, the scientific community, and the general public will benefit from the study as a tool to better understand pandemic transmission centers and identify areas where more protective measures and response actions are needed at a finer spatial resolution.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>35805472</pmid><doi>10.3390/ijerph19137811</doi><orcidid>https://orcid.org/0000-0002-1497-8611</orcidid><orcidid>https://orcid.org/0000-0001-5014-7885</orcidid><orcidid>https://orcid.org/0000-0001-9283-3947</orcidid><orcidid>https://orcid.org/0000-0001-8887-915X</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Coronaviruses COVID-19 COVID-19 - epidemiology Decision making Decision Support Techniques Disease Susceptibility Disinfection Ethiopia - epidemiology Evaluation Geographic Information Systems Health care Health risks Humans Infections Land use Multiple criterion Population density Public health Resource allocation Risk SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 Spatial discrimination Spatial resolution |
title | Evaluation of Vulnerability Status of the Infection Risk to COVID-19 Using Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA): A Case Study of Addis Ababa City, Ethiopia |
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