Computational models offer a crucial setting to test strategies to mitigate the spread of infectious diseases, providing useful insights to applied public health. To be actionable, models need to be informed by data that describe the structure  of interactions between individuals. While data coming from different sources and at different resolutions have become increasingly available, their integration into computational frameworks poses a number of challenges.

I will first give an example of how high resolution data sets can be used to build realistic agent-based models, suggest,  evaluate and compare various mitigation strategies. As such detailed data sets are however rarely available, I will discuss whether models fed with less detailed data lead to the same actionable conclusions. Moreover, I will present  recently developed methods to create surrogate data with realistic properties, that can be used to create arbitrarily long synthetic data sets, describing realistic contact patterns in various settings, to feed models of epidemic spread in a population.

Finally, while most studies are concerned on how the structure of contacts shapes the spread of a disease, I will address a reverse question: do different spreading processes unfolding on a network lead to the same propagation patterns? This has consequences in the role of models in decision-making, as many results on propagation patterns and on the identification of structures with high spreading power or to monitor in surveillance programs are typically obtained using very simplified contagion processes. Our results imply in particular that numerical simulations using simplified settings can bring important insights even in the case of a new emerging disease whose properties are not yet well known.