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This study introduces a complex network model to simulate infectious disease spread, finding that higher infection rates increase cases but network dimension can suppress transmission. The model aids public health strategies.

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Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Modeling

Background:

  • Globalization and population mobility accelerate infectious disease spread, posing public health security challenges.
  • Existing models may not fully capture complex transmission dynamics across diverse network structures.

Purpose of the Study:

  • To develop and analyze a complex network epidemic model with nonlinear incidence and quadratic transmission.
  • To investigate Turing patterns, sensitivity, and parameter identification under various network conditions.
  • To explore the impact of network dimension and structure on disease propagation, including COVID-19.

Main Methods:

  • Utilized a complex network epidemic model with nonlinear incidence and quadratic transmission.
  • Performed sensitivity analysis and parameter identification using gradient descent and Markov Chain Monte Carlo methods.
  • Simulated disease transmission on homogeneous, heterogeneous, and random networks, including COVID-19 transmission path analysis.

Main Results:

  • Identified conditions for Turing instability on different network types.
  • Demonstrated that higher infection rates correlate with more infected individuals.
  • Found that increased network dimension effectively suppresses infectious disease spread on random networks.

Conclusions:

  • The complex network model accurately simulates infectious disease transmission, offering theoretical support for public health interventions.
  • Sensitivity analysis and parameter identification highlight key factors influencing disease spread and the role of network structure.
  • Model validation with real-world data (e.g., COVID-19) supports its utility for informing scientific prevention and control strategies.