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

The last two 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. Networks used in these research are either synthetic networks or real networks with limited size and domain specific interactions. Hence, their ability to represent the world is limited. Intending to represent real-life in an urban town in high resolution, we propose a parametric multi-layer undirected weighted network model, where vertices are the individuals of a town that tend to interact locally, and edges represent transmission probability. Each layer corresponds to a different interaction that occurs daily, such as "household", "work" or "school", with their own transmission probability. Our simulations indicate that locking down "friendship" layer has the highest impact in 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.