Covid-19 severity and urban factors: investigation and recommendations based on machine learning techniques

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dc.contributor.authorQanazi, S.
dc.contributor.authorHijazi, I.
dc.contributor.authorToma, A.
dc.contributor.authorAbujayyab, S.
dc.contributor.authorDehbi, Y.
dc.contributor.authorZabadae, S.
dc.contributor.authorLif, X.
dc.date.accessioned2024-09-29T16:22:40Z
dc.date.available2024-09-29T16:22:40Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description.abstractSince March 5, 2020, the West Bank has faced a real crisis due to the Coronavirus disease 2019 (COVID-19) pandemic. It has infected 581,678 people and caused 5,382 deaths so far, which has resulted in negative impacts on public health and other aspects of daily life. Based on the data provided by the Palestinian Ministry of Health, we inferred the spatial distribution patterns of the pandemic condition in different communities using Geographic Information System (GIS) analysis for pattern and clustering by studying the impact of urban factors on the number of confirmed COVID-19 cases. Ten urban factors were selected (i.e., population, population density, aging ratio, the hierarchy of services, health services, land use, commercial ser-vices, road density, green areas, and open spaces) to check their relation to pandemic severity using a linear model, where five factors showed a globally-significant relation. Then, the Geographically Weighted Regression' model (GWR) was adopted to define their unevenly dis-tributed effects in the urban areas on the northwest bank. Among the five factors, the population factor has the most significant impact on the epidemic situation with a positive correlation. However, a negative correlation has been stated between the area of commercial services per person, population density, hierarchy of services, and health services. Finally, we provide recommendations that coordinate various urban factors to mitigate the pandemic spread. This paper will help decision-makers plan and develop different areas in Palestine and worldwide by better understanding the transmission, occurrence, and diffusion of the COVID-19 pandemic in urban areas. © 2022, An-Najah National University. All rights reserved.en_US
dc.identifier.issn2413-8568
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85138684562en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/20.500.14619/10211
dc.identifier.volume7en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAn-Najah National Universityen_US
dc.relation.ispartofPalestinian Medical and Pharmaceutical Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCOVID-19en_US
dc.subjectEpidemic analysisen_US
dc.subjectMachine learningen_US
dc.subjectRegres-sionen_US
dc.subjectUrban factorsen_US
dc.subjectUrban spatial patternsen_US
dc.titleCovid-19 severity and urban factors: investigation and recommendations based on machine learning techniquesen_US
dc.typeArticleen_US

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