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A knowledge graph-based method for epidemic contact tracing in public transportation.

Tian Chen1, Yimu Zhang2, Xinwu Qian3

  • 1Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai 201804, China.

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Summary

This study introduces a knowledge graph framework for digital contact tracing in public transport. It achieves over 96% positive tracing rates, improving on traditional methods for epidemic prevention.

Keywords:
Contact NetworkDigital Contact TracingEpidemic ControlKnowledge GraphPublic Transportation

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Area of Science:

  • Epidemiology
  • Data Science
  • Transportation Systems Engineering

Background:

  • Traditional contact tracing is slow and labor-intensive, especially during pandemics like COVID-19.
  • Digital solutions are needed for faster and more effective public health interventions in transit systems.

Purpose of the Study:

  • To develop and validate a knowledge graph-based framework for digital contact tracing in public transportation.
  • To integrate multi-source transit data for constructing accurate contact networks.
  • To model epidemic spread and assess the efficiency of the proposed tracing method.

Main Methods:

  • Utilized a trip chaining model to integrate multi-source public transportation data into a knowledge graph.
  • Extracted a contact network from the knowledge graph.
  • Developed a breadth-first search algorithm for efficient tracing of infected passengers.
  • Validated the framework using smart card transaction data from Xiamen, China.

Main Results:

  • The knowledge graph framework successfully reconstructed contact networks from transit data.
  • The digital contact tracing method demonstrated an average positive tracing rate exceeding 96%.
  • The proposed approach proved efficient for identifying contacts in public transportation.

Conclusions:

  • Knowledge graphs offer an efficient framework for digital contact tracing in public transport.
  • The developed algorithms effectively model epidemic spread and enable rapid contact identification.
  • This digital approach significantly enhances public health surveillance in transit environments.