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

BACKGROUND: 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 rec...

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Autores principales: Ankarali, Handan, Uslu, Unal, Ankarali, Seyit, Cangur, Sengul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Medical Association Of Malawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9356527/
https://www.ncbi.nlm.nih.gov/pubmed/35991817
http://dx.doi.org/10.4314/mmj.v34i2.2
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author Ankarali, Handan
Uslu, Unal
Ankarali, Seyit
Cangur, Sengul
author_facet Ankarali, Handan
Uslu, Unal
Ankarali, Seyit
Cangur, Sengul
author_sort Ankarali, Handan
collection PubMed
description BACKGROUND: 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.
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spelling pubmed-93565272022-08-18 Assessment of the dissimilarities of totally 186 countries and regions according to COVID-19 indicators at the end of March 2020 Ankarali, Handan Uslu, Unal Ankarali, Seyit Cangur, Sengul Malawi Med J Original Research BACKGROUND: 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. The Medical Association Of Malawi 2022-06 /pmc/articles/PMC9356527/ /pubmed/35991817 http://dx.doi.org/10.4314/mmj.v34i2.2 Text en © 2022 The College of Medicine and the Medical Association of Malawi. https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under the Creative Commons Attribution 4.0 International License. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) )
spellingShingle Original Research
Ankarali, Handan
Uslu, Unal
Ankarali, Seyit
Cangur, Sengul
Assessment of the dissimilarities of totally 186 countries and regions according to COVID-19 indicators at the end of March 2020
title Assessment of the dissimilarities of totally 186 countries and regions according to COVID-19 indicators at the end of March 2020
title_full Assessment of the dissimilarities of totally 186 countries and regions according to COVID-19 indicators at the end of March 2020
title_fullStr Assessment of the dissimilarities of totally 186 countries and regions according to COVID-19 indicators at the end of March 2020
title_full_unstemmed Assessment of the dissimilarities of totally 186 countries and regions according to COVID-19 indicators at the end of March 2020
title_short Assessment of the dissimilarities of totally 186 countries and regions according to COVID-19 indicators at the end of March 2020
title_sort assessment of the dissimilarities of totally 186 countries and regions according to covid-19 indicators at the end of march 2020
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9356527/
https://www.ncbi.nlm.nih.gov/pubmed/35991817
http://dx.doi.org/10.4314/mmj.v34i2.2
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