Clustering analysis of countries using the COVID-19 cases dataset

Vasilios Zarikas; Stavros G Poulopoulos; Zoe Gareiou; Efthimios Zervas

doi:10.1016/j.dib.2020.105787

Clustering analysis of countries using the COVID-19 cases dataset

Data Brief. 2020 May 29:31:105787. doi: 10.1016/j.dib.2020.105787. eCollection 2020 Aug.

Authors

Vasilios Zarikas^{1

2}, Stavros G Poulopoulos³, Zoe Gareiou⁴, Efthimios Zervas⁴

Affiliations

¹ School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan.
² General Department, University of Thessaly, Lamia, Greece.
³ Environmental Science & Technology Group (ESTg), Chemical and Materials Engineering Department, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan.
⁴ School of Science and Technology, Hellenic Open University, Patra, Greece.

Abstract

There is a worldwide effort of the research community to explore the medical, economic and sociologic impact of the COVID-19 pandemic. Many different disciplines try to find solutions and drive strategies to a great variety of different very crucial problems. The present study presents a novel analysis which results to clustering countries with respect to active cases, active cases per population and active cases per population and per area based on Johns Hopkins epidemiological data. The presented cluster results could be useful to a variety of different policy makers, such as physicians and managers of the health sector, economy/finance experts, politicians and even to sociologists. In addition, our work suggests a new specially designed clustering algorithm adapted to the request for comparison of the various COVID time-series of different countries.

Keywords: Clustering; Health policy; Hierarchical method; SARS-CoV-2; Time series.