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Towards Using Graph Analytics for Tracking Covid-19.

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Summary
This summary is machine-generated.

This study introduces a graph-based Spectral Clustering approach for detecting COVID-19 propagation patterns. It groups countries with similar transmission behaviors, offering an unsupervised alternative to traditional classification methods.

Keywords:
Communities detectionCoronavirusCovid-19Graph analyticsMachine learningSpectral clustering

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Area of Science:

  • Computer Science
  • Data Science
  • Epidemiology

Background:

  • Graph analytics are state-of-the-art for community detection in machine learning.
  • Spectral Clustering (SC) algorithms show promise in overcoming limitations of existing community detection methods.
  • Traditional multiclass classification requires predefining output clusters, which can be a limitation.

Purpose of the Study:

  • To introduce a novel graph-based approach for community detection using COVID-19 country datasets.
  • To leverage Spectral Clustering (SC) as an unsupervised method, eliminating the need for predefined clusters.
  • To group countries exhibiting similar COVID-19 propagation behaviors.

Main Methods:

  • Utilized a graph-based approach for community detection.
  • Applied Spectral Clustering (SC), an unsupervised machine learning algorithm.
  • Incorporated an automatic estimation method for the number of clusters (k).
  • Analyzed dynamic statistical data for over 200 countries.

Main Results:

  • Successfully grouped countries based on similar COVID-19 propagation patterns.
  • Demonstrated the effectiveness of SC in an unsupervised community detection task.
  • Provided a method for identifying country clusters with comparable disease spread dynamics.

Conclusions:

  • The proposed graph-based SC approach offers an effective unsupervised method for analyzing COVID-19 propagation patterns.
  • This method overcomes limitations of multiclass classification by not requiring predefinition of clusters.
  • The findings facilitate a better understanding of country-level disease transmission similarities.