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Epidemic spreading in heterogeneous networks with recurrent mobility patterns.

Liang Feng1, Qianchuan Zhao1, Cangqi Zhou2

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This study models epidemic spreading in networks using a Markov chain. It reveals how mobility and network structure impact disease transmission, offering insights for public health policy.

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Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Modeling

Background:

  • Network structure and human mobility significantly influence epidemic spreading.
  • Understanding these dynamics is crucial for effective disease control.

Purpose of the Study:

  • To propose a discrete-time Markov chain model for susceptible-infected-susceptible (SIS) epidemic dynamics in heterogeneous networks.
  • To analyze the impact of mobility and isolation on the epidemic threshold.
  • To investigate how network characteristics affect epidemic spreading.

Main Methods:

  • Developed a discrete-time Markov chain model incorporating two location types: residences and common places.
  • Implemented different infection mechanisms for each location type.
  • Conducted theoretical analysis and numerical simulations to validate the model.

Main Results:

  • Theoretical results demonstrate the influence of mobility probability and isolation on the epidemic threshold.
  • Numerical simulations confirm the analytical findings.
  • Observed that the dominance of different residence types can reverse with varying mobility probabilities in certain networks.

Conclusions:

  • The proposed model provides a framework for understanding epidemic dynamics in complex networks.
  • Findings highlight the critical role of human mobility and network heterogeneity in disease transmission.
  • Results offer valuable insights for public health interventions and policy-making to prevent epidemics.