  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.
