Assessment of the dissimilarities of totally 186 countries and regions according to COVID-19 indicators at the end of March 2020

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dc.authoridUSLU, UNAL/0000-0003-3953-7131
dc.authorwosidUSLU, UNAL/H-1226-2011
dc.contributor.authorAnkaralı, Handan
dc.contributor.authorUslu, Ünal
dc.contributor.authorAnkaralı, Seyit
dc.contributor.authorCangür, Şengül
dc.date.accessioned2023-07-26T11:54:14Z
dc.date.available2023-07-26T11:54:14Z
dc.date.issued2022
dc.departmentDÜ, Ziraat Fakültesi, Biyosistem Mühendisliği Bölümüen_US
dc.description.abstractBackground This study is aimed at evaluating the relationship between the number of days elapsed since a country's first case(s) of coronavirus disease 2019 (COVID-19), the total number of tests conducted, and outbreak indicators such as the total numbers of cases, deaths, and patients who recovered. The study compares COVID-19 indicators among countries and clusters them according to similarities in the indicators. Methods Descriptive statistics of the indicators were computed and the results were presented in figures and tables. A fuzzy c-means clustering algorithm was used to cluster/group the countries according to the similarities in the total numbers of patients who recovered, deaths, and active cases. Results The highest numbers of COVID-19 cases were found in Gibraltar, Spain, Switzerland, Liechtenstein and Italy were also of that order with about 1500 cases per million population. Spain and Italy had the highest total number of deaths, which were about 140 and 165 per million population, respectively. In Japan, where exposure to the causative virus was longer than in most other countries, the total number of deaths per million population was less than 0.5. According to cluster analysis, the total numbers of deaths, patients who recovered, and active cases were higher in Western countries, especially in central and southern European countries, which had the highest numbers when compared with other countries. Conclusion There may be various reasons for the differences between the clusters obtained by fuzzy c-means clustering. These include quarantine measures, climatic conditions, economic levels, health policies, and the duration of the fight against the outbreak.en_US
dc.identifier.doi10.4314/mmj.v34i2.2
dc.identifier.endpage86en_US
dc.identifier.issn1995-7262
dc.identifier.issn1995-7270
dc.identifier.issue2en_US
dc.identifier.pmid35991817en_US
dc.identifier.scopus2-s2.0-85135251222en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage73en_US
dc.identifier.urihttps://doi.org/10.4314/mmj.v34i2.2
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12771
dc.identifier.volume34en_US
dc.identifier.wosWOS:000863008400002en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorCangür, Şengül
dc.language.isoenen_US
dc.publisherKamuzu Univ Health Sciences - Kuhesen_US
dc.relation.ispartofMalawi Medical Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectCovid-19; Total Number Of Cases; Total Number Of Deaths; Outbreak; Clusteringen_US
dc.subjectFuzzyen_US
dc.titleAssessment of the dissimilarities of totally 186 countries and regions according to COVID-19 indicators at the end of March 2020en_US
dc.typeArticleen_US

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