Dynamic multiplex social network models on multiple time scales for simulating contact formation and patterns in epidemic spread

This contribution presents a model for dynamic networks of physical contacts among large populations and their application for reproducing complex patterns in epidemic spread. The networks are constructed from statistical data on demography, geography, organizational structure and contact behavior. Due to the heterogeneous nature of the data and by construction, rich topological characteristics such as overlapping communities, layering along multiple dimensions and multiplex dynamics on different time scales can be observed. The generated dynamic networks can furthermore be regarded as subgraphs or derivatives of latent social networks. General results and observations form social network theory apply naturally and are used for explaining dynamic effects in epidemics. An exemplaric analysis investigates the impact of weak ties and effects of communities with decreased immunization on epidemic spread. Optimized implementation and visualization techniques turn out to be a key asset for dynamic simulation of contacts within large populations.