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A Social Network Analysis Approach to COVID-19 Community Detection Techniques.

Tanupriya Choudhury1, Rohini Arunachalam2, Abhirup Khanna3

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Machine learning and social network analysis reveal COVID-19 community clusters. Identifying influential sub-communities can improve public health information dissemination.

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Area of Science:

  • Network analysis
  • Machine learning
  • Public health

Background:

  • Assessed the geographical distribution of Tambaram, Chennai, and its public health care units.
  • Examined the spatial distribution and spatiotemporal clustering of public health care units over four months in the Tambaram zone.

Purpose of the Study:

  • To apply machine learning and social network analysis for COVID-19 awareness.
  • To identify and analyze community structures within geographical health networks.

Main Methods:

  • Employed machine learning for network analysis and community detection.
  • Utilized social network analysis to describe clustering and classification processes.
  • Conducted an empirical evaluation of two search strategies on a synthetic network.
  • Analyzed spatial information and sociogram structures for graph segmentation based on clustering coefficients.

Main Results:

  • Identified cohesive blocks with a density value of 5.86 for cluster two, accounting for 74.2% of the network.
  • Demonstrated that sub-communities possess greater influence for information sharing.

Conclusions:

  • Machine learning and network analysis are effective tools for understanding health-related networks.
  • Findings suggest leveraging influential sub-communities for targeted public health messaging and COVID-19 awareness campaigns.