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 also discuss whether models fed with less detailed data lead to the same actionable conclusions. Finally, while most studies focus 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. I will conclude with some perspectives on new avenues in the modeling of spreading phenomena.

Network analysis is traditionally concerned with relations between people and their consequences; what sets network modelling apart is not the substantive topic, though, but an empirical and conceptual perspective. We embrace and analyse inter-dependence between different people and different ties, thereby shifting explanations from variables to social relations and structures. However, the analysis of interdependence is mostly confined to relatively small social systems, that is, complete social networks in some bounded context. In this presentation, I discuss how the conceptual focus on inter-dependence can be extended to larger systems by studying networks of social positions in which individuals or pairs of individuals constitute ties rather than nodes – for example, residents moving between neighbourhoods of a city span a “mobility network” between these neighbourhoods. Here, each move is embedded in the mobility of other people (i.e. dependent); one example of the consequence of such inter-dependence is described in the Schelling model of residential segregation. Focussing mostly on examples using labour market positions, in particular occupations, I propose that a wide range of insights can be gained from studying interdependence in such networks; these insights go beyond what can be learned from a traditional variable-based approach. I discuss networks in which occupations are connected by parent-child dyads, by siblings, by spouses, and by individuals over the life-course. The insights we can gain relate to themes classically studied by network researchers, such as social foci, social influence, or relational sociology, but also to broader socio-economic questions including inequality, social closure, and the inheritance of advantage.