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Network induction for epidemic profiles with a novel representation.

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This study introduces an evolutionary computation method to discover contact networks for epidemic modeling. It enhances network search by adding local adjustments and a

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Area of Science:

  • Epidemiology
  • Network Science
  • Computational Biology

Background:

  • Contact networks are crucial for modeling epidemic spread.
  • Existing research primarily analyzes predefined graphs.
  • The inverse problem of generating graphs with specific epidemic properties is underexplored.

Purpose of the Study:

  • To develop and test an evolutionary computation approach for searching the space of contact networks.
  • To improve representations for evolving potential contact networks.
  • To address the challenge of searching complex network spaces for epidemic modeling.

Main Methods:

  • Extends and tests a representation for searching contact networks using evolutionary computation.
  • Introduces an operator for local adjustments to network connectivity.
  • Incorporates an operator that allows for periods of inactivity during network construction.

Main Results:

  • Demonstrates the effectiveness of the enhanced representation in evolving contact networks.
  • Shows substantial benefits from incorporating inactivity periods, allowing for automatic adjustment of commands.
  • Successfully tested the network induction method on two distinct tasks.

Conclusions:

  • The proposed evolutionary computation method with enhanced representation is effective for discovering contact networks with desired epidemic properties.
  • Local connectivity adjustments and strategic inactivity are key improvements for network evolution.
  • This approach advances the inverse problem in network science for applications in epidemiology.