Edge-Based Compartmental Modelling of an SIR Epidemic on a Dual-Layer Static-Dynamic Multiplex Network with Tunable...

Area of Science:

• Epidemiology
• Network Science
• Mathematical Modeling

Background:

• Disease transmission is influenced by population connection patterns.
• Real-world networks exhibit both stable community structures and dynamic, transient interactions.

Purpose of the Study:

• To develop and analyze a dual-layer multiplex network model for epidemic spread.
• To incorporate static (community) and dynamic (transient) connection heterogeneities into disease modeling.
• To derive and validate the basic reproduction number for epidemics on such networks.

Main Methods:

• Utilized an edge-based compartmental modeling approach.
• Developed a dual-layer static-dynamic multiplex network framework.
• Derived epidemic evolution equations and the basic reproduction number.
• Validated the model through convergence analysis and stochastic simulations.

Main Results:

• The model accurately describes susceptible-infected-recovered epidemic dynamics on multiplex networks.
• Derived equations were validated against existing models and simulations.
• The basic reproduction number was validated against final epidemic sizes.
• Explored the impact of network attributes and parameters on epidemic spread.

Conclusions:

• The dual-layer network model provides a robust framework for studying epidemic dynamics with realistic connection heterogeneities.
• The derived basic reproduction number is a key metric for understanding disease invasion potential.
• The model offers insights into how network structure influences disease spread and control strategies.