Title: Hierarchical cluster analysis of the morbidity and mortality of COVID-19 across 206 countries, territories and areas

Authors: Donald Douglas Atsa'am; Ruth Wario

Addresses: Department of Computer Science and Informatics, Faculty of Natural and Agricultural Sciences, University of the Free State, Qwaqwa, South Africa ' Department of Computer Science and Informatics, Faculty of Natural and Agricultural Sciences, University of the Free State, South Africa

Abstract: This research deployed the agglomerative hierarchical clustering to extract clusters from the coronavirus disease 2019 (COVID-19) data based on the morbidity and mortality of the novel virus across 206 countries, territories and areas. As of 2 April, 2020, a total of 896,475 confirmed cases were reported across the world. Three clusters were extracted from the data on the bases of morbidity and mortality of COVID-19. These include: low-confirmed-cases, low-new-cases, low-deaths and low-new-deaths countries [cluster 1]; medium-confirmed-cases, low-new-cases, medium-deaths, and medium-new-deaths countries [cluster 2]; high-confirmed-cases, high-new-cases, high-deaths, and high-new-deaths countries [cluster 3]. It is recommended that, to contain the pandemic, countries within a cluster should cooperate, share information and learn from mistakes or strategies (as the case may be) of the countries in other clusters. Among other benefits, this can prevent countries within the low-confirmed-cases cluster from progressing to the high-confirmed-cases cluster.

Keywords: COVID-19; morbidity; mortality; hierarchical clustering; data mining.

DOI: 10.1504/IJMEI.2022.121128

International Journal of Medical Engineering and Informatics, 2022 Vol.14 No.2, pp.125 - 133

Received: 21 Apr 2020
Accepted: 14 Jun 2020
Published online: 28 Feb 2022 *

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