Clustering of countries in terms of deaths and cases of COVID-19
Introduction: The novel coronavirus ‘Severe Acute Respiratory Syndrome Coronavirus Type 2’ (SARS- CoV-2), responsible for the disease termed as ‘Coronavirus disease 2019’ (COVID-19 pandemic) first broke out in Wuhan, Hubei province, mainland China. With its rapid spread and reports revealing the cru...
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Edizioni FS
2020-12-01
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| Series: | Journal of Health and Social Sciences |
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| Online Access: | https://journalhss.com/wp-content/uploads/jhss_587-594.pdf |
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| author | Ozge PASIN Tugce PASIN |
| author_facet | Ozge PASIN Tugce PASIN |
| author_sort | Ozge PASIN |
| collection | DOAJ |
| description | Introduction: The novel coronavirus ‘Severe Acute Respiratory Syndrome Coronavirus Type 2’ (SARS- CoV-2), responsible for the disease termed as ‘Coronavirus disease 2019’ (COVID-19 pandemic) first broke out in Wuhan, Hubei province, mainland China. With its rapid spread and reports revealing the crucial consequences of this spread, countries adopted strict measures to tackle the disease. The objective of this study is to cluster the various countries describing the course of the COVID-19 outbreak.
Methods: The data used was obtained from the Worldometers' website on October 22, 2020 in its most current form for 191 countries. The number of total cases and deaths were used. The numbers were calcula- ted considering population sizes. The total deaths / 1million population and total cases/ 1million population were used for clustering. For clustering k-means clustering method and elbow method were used. Also the two-step clustering method was used for the clustering process.
Results: As a result, Armenia, Aruba, Bahrain, French Guiana, Israel, Kuwait, Montenegro, Oman, Qatar, San Marino were a single cluster apart from other countries for two-step clustering. Also in k-means clu- stering Aruba, Bahrain, French Guiana, Israel and Qatar was a single cluster apart from other countries for k-means clustering.
Conclusion: This study will be of great importance, when showing the differences among countries in terms of total deaths and cases in terms of population. Proper use of these data will help states take precautions regarding COVID-19. |
| format | Article |
| id | doaj-art-3614866813c249a1b1759c3c0769e5d5 |
| institution | Kabale University |
| issn | 2499-5886 2499-2240 |
| language | English |
| publishDate | 2020-12-01 |
| publisher | Edizioni FS |
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| series | Journal of Health and Social Sciences |
| spelling | doaj-art-3614866813c249a1b1759c3c0769e5d52025-01-18T18:20:29ZengEdizioni FSJournal of Health and Social Sciences2499-58862499-22402020-12-015458759410.19204/2020/clst15Clustering of countries in terms of deaths and cases of COVID-19Ozge PASIN0Tugce PASIN1PhD, Department of Biostatistics, Faculty of Medicine, Istanbul University, Istanbul, TurkeyMD, Department of Physical Medicine and Rehabilitation, Istanbul Goztepe Training and Research Hospital, Istanbul, TurkeyIntroduction: The novel coronavirus ‘Severe Acute Respiratory Syndrome Coronavirus Type 2’ (SARS- CoV-2), responsible for the disease termed as ‘Coronavirus disease 2019’ (COVID-19 pandemic) first broke out in Wuhan, Hubei province, mainland China. With its rapid spread and reports revealing the crucial consequences of this spread, countries adopted strict measures to tackle the disease. The objective of this study is to cluster the various countries describing the course of the COVID-19 outbreak. Methods: The data used was obtained from the Worldometers' website on October 22, 2020 in its most current form for 191 countries. The number of total cases and deaths were used. The numbers were calcula- ted considering population sizes. The total deaths / 1million population and total cases/ 1million population were used for clustering. For clustering k-means clustering method and elbow method were used. Also the two-step clustering method was used for the clustering process. Results: As a result, Armenia, Aruba, Bahrain, French Guiana, Israel, Kuwait, Montenegro, Oman, Qatar, San Marino were a single cluster apart from other countries for two-step clustering. Also in k-means clu- stering Aruba, Bahrain, French Guiana, Israel and Qatar was a single cluster apart from other countries for k-means clustering. Conclusion: This study will be of great importance, when showing the differences among countries in terms of total deaths and cases in terms of population. Proper use of these data will help states take precautions regarding COVID-19.https://journalhss.com/wp-content/uploads/jhss_587-594.pdfclustering; coronavirus; covid-19: k-means; statistics |
| spellingShingle | Ozge PASIN Tugce PASIN Clustering of countries in terms of deaths and cases of COVID-19 Journal of Health and Social Sciences clustering; coronavirus; covid-19: k-means; statistics |
| title | Clustering of countries in terms of deaths and cases of COVID-19 |
| title_full | Clustering of countries in terms of deaths and cases of COVID-19 |
| title_fullStr | Clustering of countries in terms of deaths and cases of COVID-19 |
| title_full_unstemmed | Clustering of countries in terms of deaths and cases of COVID-19 |
| title_short | Clustering of countries in terms of deaths and cases of COVID-19 |
| title_sort | clustering of countries in terms of deaths and cases of covid 19 |
| topic | clustering; coronavirus; covid-19: k-means; statistics |
| url | https://journalhss.com/wp-content/uploads/jhss_587-594.pdf |
| work_keys_str_mv | AT ozgepasin clusteringofcountriesintermsofdeathsandcasesofcovid19 AT tugcepasin clusteringofcountriesintermsofdeathsandcasesofcovid19 |