Epidemic fronts in complex networks with metapopulation structure

Area of Science:

• Epidemiology
• Network Science
• Mathematical Modeling

Background:

• Infection dynamics are studied on complex networks, highlighting contact heterogeneity's role in disease spread.
• Metapopulations model spatially extended systems, but integrating them with network complexity is challenging.

Purpose of the Study:

• To combine complex networks and metapopulation paradigms using multitype networks for disease modeling.
• To analyze epidemic front propagation on a chain of interconnected networks to understand macroscale spread dependence on microscale topology.

Main Methods:

• Utilized a generalized Miller-Volz mean-field approximation for SIR dynamics on multitype networks.
• Applied front propagation formalism to derive effective transport coefficients (speed, wavelength, diffusion) for epidemic fronts.
• Investigated epidemic thresholds and front profiles across various network configurations.

Main Results:

• Derived effective transport coefficients (asymptotic speed, characteristic wavelength, diffusion coefficient) for epidemic fronts.
• Analyzed the dependence of these coefficients on the underlying graph structure.
• Determined the epidemic threshold for the system and characterized front profiles.

Conclusions:

• Multitype networks offer a framework to integrate contact complexity and spatial structure in epidemic modeling.
• Understanding microscale network topology is crucial for predicting macroscale disease spread dynamics.
• The derived transport coefficients and epidemic threshold provide key insights into epidemic front behavior on structured populations.