Multi-layer network approach in modeling epidemics in an urban town

Area of Science:

• Epidemiology
• Network Science
• Computational Modeling

Background:

• The COVID-19 pandemic highlighted the need for advanced epidemic forecasting.
• Existing models often lack the granularity to represent complex, real-world social interactions.

Purpose of the Study:

• To develop a high-resolution, multi-layer network model for urban epidemic simulation.
• To evaluate the impact of interventions targeting different social interaction layers.

Main Methods:

• Proposed a novel parametric multi-layer network generator.
• Implemented Susceptible-Infected-Removed (SIR) simulations on the generated networks.
• Analyzed the effect of isolating specific network layers.

Main Results:

• The multi-layer network model effectively represents diverse daily interactions (household, work, school).
• Simulations demonstrated that interventions targeting the "friendship" layer had the most significant impact on reducing epidemic spread.
• The parametric network generator allows for customizable and scalable epidemic modeling.

Conclusions:

• Multi-layer network models offer a more realistic approach to understanding epidemic dynamics in urban environments.
• Targeting social connection layers, particularly friendships, is a crucial strategy for pandemic mitigation.
• This work provides a framework for improved epidemic preparedness and response planning.