Abstract
Defining accurate and flexible models for real-world networks of human beings is instrumental to understand the observed properties of phenomena taking place across those networks and to support computer simulations of dynamic processes of interest for several areas of research – including computational epidemiology, which is recently high on the agenda. In this paper we present a flexible model to generate age-stratified and geo-referenced synthetic social networks on the basis of widely available aggregated demographic data and, possibly, of estimated age-based social mixing patterns. Using the Italian city of Florence as a case study, we characterize our network model under selected configurations and we show its potential as a building block for the simulation of infections’ propagation. A fully operational and parametric implementation of our model is released as open-source.
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Notes
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The source code is released under the GPL v3 at gitlab.com/cranic-group/usn.
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Throughout this paper, we use the parameterization \(\mathcal {LN}\left( \lambda ,\sigma ^2\right) \) where \(\lambda \) and \(\sigma ^2\) are the mean and variance of the associated Normal distribution.
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ISTAT data used in this paper are available at https://www.demo.istat.it/pop2020.
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The pair (various, various) covers all cases other than the previous ones.
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Acknowledgment
The authors thank the municipality of Florence for the kind support provided and Francesca Colaiori for useful discussions.
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Guarino, S. et al. (2021). A Model for Urban Social Networks. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12744. Springer, Cham. https://doi.org/10.1007/978-3-030-77967-2_23
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