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

TL;DR

This paper introduces a multi-layer network model to simulate epidemic spread in urban settings, highlighting the significant impact of social interaction layers like friendship on controlling outbreaks.

Contribution

It presents a novel parametric network generator and demonstrates the effects of different social layers on epidemic dynamics through SIR simulations.

Findings

Lockdowns on the 'friendship' layer most effectively slow epidemics.

The multi-layer model captures complex social interactions more realistically.

Simulations provide insights for targeted intervention strategies.

Abstract

The last three years have been an extraordinary time with the Covid-19 pandemic killing millions, affecting and distressing billions of people worldwide. Authorities took various measures such as turning school and work to remote and prohibiting social relations via curfews. In order to mitigate the negative impact of the epidemics, researchers tried to estimate the future of the pandemic for different scenarios, using forecasting techniques and epidemics simulations on networks. Intending to better represent the real-life in an urban town in high resolution, we propose a novel multi-layer network model, where each layer corresponds to a different interaction that occurs daily, such as "household", "work" or "school". Our simulations indicate that locking down "friendship" layer has the highest impact on slowing down epidemics. Hence, our contributions are twofold, first we propose a parametric network generator model; second, we run SIR simulations on it and show the impact of layers.