Fuzzy Clustering Methods to Identify the Epidemiological Situation and Its Changes in European Countries during...

Area of Science:

• Epidemiology
• Data Science
• Public Health

Background:

• The COVID-19 pandemic presented complex epidemiological dynamics requiring advanced analytical methods.
• Understanding country-specific epidemic trajectories is crucial for effective public health interventions.

Purpose of the Study:

• To identify and classify distinct COVID-19 epidemic states across European countries.
• To analyze transitions between these states using fuzzy clustering.
• To evaluate the uncertainty associated with epidemic state classifications.

Main Methods:

• Utilized cross-sectional time series data from the European Centre for Disease Prevention and Control.
• Applied the fuzzy c-means clustering algorithm for epidemiological risk assessment.
• Employed the entropy index to quantify classification fuzziness and state uncertainty.

Main Results:

• Identified three primary COVID-19 epidemic states in Europe: stabilization, destabilization, and expansion.
• Successfully determined the timing of transitions between these epidemic states.
• Observed and quantified fluctuations during state changes, highlighting dynamic epidemic behavior.

Conclusions:

• Fuzzy clustering provides a robust method for identifying and tracking COVID-19 epidemic states.
• The methodology offers insights into epidemic dynamics, crucial for policy-making.
• Results are applicable to other countries and inform strategies for pandemic mitigation.