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Clustering-based methodology for comparing multi-characteristic epidemiological dynamics with application to COVID-19
Alexander Kirpich1, Aleksandr Shishkin1, Pema Lhewa1
1Department of Population Health Sciences, Georgia State University, Atlanta, GA, United States.
Royal Society Open Science
|September 26, 2025
Summary
This study explored how COVID-19 dynamics relate to public health and sociodemographic factors across European countries. Findings reveal varied agreements between different factors, highlighting complex relationships influencing disease spread.
Area of Science:
- Epidemiology
- Public Health
- Sociology
Background:
- Understanding the interplay between COVID-19 dynamics and country-specific factors is crucial for effective public health strategies.
- Previous research has not fully elucidated the complex relationships between epidemiological trends and sociodemographic characteristics.
Purpose of the Study:
- To investigate if countries with similar COVID-19 dynamics share common public health and sociodemographic profiles.
- To analyze the associations between clusterings based on different epidemiological, public health, and sociodemographic variables.
Main Methods:
- A clustering-based approach was applied to data from 42 European countries.
- Six key characteristics were analyzed: COVID-19 incidence, mortality, vaccination rates, SARS-CoV-2 genetic diversity, cross-country mobility, and sociodemographic data.
- Hierarchical clustering and correlation measures (cophenetic, Baker's Gamma) were used to assess agreement between clusterings.
Main Results:
- Distinct patterns of agreement were observed between different clusterings.
- Vaccination clustering showed moderate agreement with incidence but not mortality.
- Incidence clustering aligned with population health, genetic diversity, and sociodemographic parameters, while mortality clustering agreed only with population health.
Conclusions:
- The study highlights that different factors driving COVID-19 dynamics exhibit varied associations across European nations.
- Cluster-based methods offer utility in analyzing time-series data for epidemiological disparities.
- Findings provide insights into the complex mechanisms underlying variations in disease spread and outcomes.
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