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

dc.authorid0000-0003-3953-7131
dc.authorid0000-0002-3613-0523
dc.authorid0000-0003-3752-0846
dc.contributor.authorAnkaralı, Handan
dc.contributor.authorUslu, Unal
dc.contributor.authorAnkaralı, Seyit
dc.contributor.authorCangur, Sengul
dc.date.accessioned2025-05-10T19:31:21Z
dc.date.issued2022
dc.departmentİMÜ, Fakülteler, Temel Tıp Bilimleri Bölümü
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.
dc.identifier.doi10.4314/mmj.v34i2.2
dc.identifier.endpage86
dc.identifier.issn1995-7262
dc.identifier.issn1995-7270
dc.identifier.issue2
dc.identifier.pmid35991817
dc.identifier.scopus2-s2.0-85135251222
dc.identifier.scopusqualityQ2
dc.identifier.startpage73
dc.identifier.urihttps://doi.org/10.4314/mmj.v34i2.2
dc.identifier.urihttps://hdl.handle.net/20.500.14730/7906
dc.identifier.volume34
dc.identifier.wosWOS:000863008400002
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherKamuzu Univ Health Sciences - Kuhes
dc.relation.ispartofMalawi Medical Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250302
dc.subjectCOVID-19
dc.subjecttotal number of cases
dc.subjecttotal number of deaths
dc.subjectoutbreak
dc.subjectclustering
dc.titleAssessment of the dissimilarities of totally 186 countries and regions according to COVID-19 indicators at the end of March 2020
dc.typeArticle

Dosyalar

Orijinal paket

Listeleniyor 1 - 1 / 1
Yükleniyor...
Küçük Resim
İsim:
7906.pdf
Boyut:
4.13 MB
Biçim:
Adobe Portable Document Format