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Source detection in epidemic dynamics on hypergraphs using a dynamic message passing algorithm.

Qiao Ke1, Naoki Masuda2,3,4, Zhen Jin5

  • 1Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, China.

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This study introduces a new message passing algorithm (HDMPN) for identifying infectious disease origins. HDMPN improves source detection by considering group interactions within hypergraphs, outperforming traditional methods.

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Area of Science:

  • Epidemiology
  • Network Science
  • Computational Biology

Background:

  • Source detection is vital for managing infectious disease outbreaks and implementing control measures.
  • Traditional methods often rely on pairwise networks, neglecting complex group interactions.
  • Hypergraph representations are increasingly recognized for capturing group-based transmission patterns.

Purpose of the Study:

  • To develop a novel message passing algorithm for accurate source detection in infectious diseases.
  • To incorporate group interaction information, represented by hypergraphs, into source detection models.
  • To evaluate the performance of the proposed algorithm against existing methods.

Main Methods:

  • A message passing algorithm, termed HDMPN (Hypergraph-based Dynamic Message Passing Network), was developed.
  • The algorithm modifies likelihood maximization by utilizing the proportion of infectious neighbors within hyperedges.
  • Stochastic susceptible-infectious dynamics with correlated infections within hyperedges were modeled.

Main Results:

  • The HDMPN algorithm demonstrated superior performance in source detection compared to benchmarks.
  • Incorporating hyperedge information significantly improved the accuracy of identifying disease origins.
  • The proposed method effectively captures correlated infection events within group interactions.

Conclusions:

  • The HDMPN algorithm offers a more accurate approach to source detection by accounting for group transmission dynamics.
  • Hypergraph representations are crucial for understanding and modeling complex epidemic propagation.
  • This work advances the field of infectious disease modeling and source detection.