Multi-layer network approach in modeling epidemics in an urban town
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The last 2 years have been an extraordinary time with Covid-19 pandemic killing millions, affecting and distressing billions of people worldwide. Countries and towns took various measures such as turning school and work to remote and prohibiting social relations via curfews. In order to estimate the impact of these lockdown scenarios, we propose a multi-layer undirected weighted network, where vertices are the individuals of a town that tend to interact locally. In this network, there are seven layers such as "household", "work", "school", and "friends". Edges in a layer represent interactions that can cause a disease to spread. Depending on the type and intensity of the interactions, transmission probabilities are assigned to edges. With this layered-architecture, various lock down scenarios can be simulated by adding or removing layers. Our simulations indicate that locking down "friends" layer has the highest impact.
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