Clustering analysis of countries using the COVID-19 cases dataset

Area of Science:

• Epidemiology
• Data Science
• Public Health

Background:

• The COVID-19 pandemic has profound medical, economic, and social impacts globally.
• Diverse research disciplines are actively seeking solutions and strategies to address critical pandemic-related issues.

Purpose of the Study:

• To cluster countries based on COVID-19 active cases, active cases per population, and active cases per population and area.
• To present a novel analysis for informing various policymakers, including health sector professionals, economists, politicians, and sociologists.
• To introduce a specially designed clustering algorithm for comparing diverse COVID-19 time-series data across countries.

Main Methods:

• Utilized Johns Hopkins epidemiological data for analysis.
• Applied a novel clustering approach to categorize countries based on active case metrics.
• Developed a specialized algorithm for time-series comparison of COVID-19 data.

Main Results:

• Identified distinct clusters of countries based on active COVID-19 case data, per capita, and per area.
• Demonstrated the utility of clustering for understanding the epidemiological landscape of the pandemic.
• Successfully adapted a clustering algorithm for effective comparison of international COVID-19 time-series.

Conclusions:

• The clustering results offer valuable insights for diverse policy-making bodies.
• The developed clustering algorithm provides a new tool for analyzing and comparing international pandemic data.
• This research contributes to a better understanding of the COVID-19 pandemic's geographical and temporal dynamics.