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Interacting epidemic spreading on complex networks shows cooperative effects, leading to abrupt transitions. A message-passing approach accurately predicts model behavior and identifies optimal seeding nodes for maximizing outbreaks.

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Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Modeling

Background:

  • Spreading processes on complex networks are crucial.
  • Cooperative effects between diseases can alter epidemic dynamics, causing abrupt transitions and hysteresis.

Purpose of the Study:

  • To investigate the interaction of two pathogens on a network.
  • To develop accurate predictions for epidemic spreading models.
  • To identify optimal initial seeding locations for maximizing outbreak probability.

Main Methods:

  • Utilized the message-passing approach for theoretical analysis.
  • Conducted numerical simulations on homogeneous and heterogeneous synthetic networks.

Main Results:

  • The message-passing approach accurately predicts model behavior across the entire phase diagram for various network structures.
  • Simulations confirmed the high accuracy of the theoretical predictions.
  • The message-passing approach effectively identifies optimal seeding nodes to maximize outbreak probability.

Conclusions:

  • Cooperative effects in interacting epidemic models significantly influence spreading dynamics.
  • The message-passing approach is a powerful tool for analyzing complex spreading phenomena on networks.
  • This method provides accurate predictions and practical solutions for optimizing epidemic control strategies.