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Technological advances have led to a strong increase in the number of datacollection efforts aimed at measuring co-presence of individuals at differentspatial resolutions. It is however unclear how much co-presence data can informus on actual face-to-face contacts, of particular interest to study thestructure of a population in social groups or for use in data-driven models ofinformation or epidemic spreading processes. Here, we address this issue byleveraging data sets containing high resolution face-to-face contacts as wellas a coarser spatial localisation of individuals, both temporally resolved, invarious contexts. The co-presence and the face-to-face contact temporalnetworks share a number of structural and statistical features, but the formeris (by definition) much denser than the latter. We thus consider severaldown-sampling methods that generate surrogate contact networks from theco-presence signal and compare them with the real face-to-face data. We showthat these surrogate networks reproduce some features of the real data but areonly partially able to identify the most central nodes of the face-to-facenetwork. We then address the issue of using such down-sampled co-presence datain data-driven simulations of epidemic processes, and in identifying efficientcontainment strategies. We show that the performance of the various samplingmethods strongly varies depending on context. We discuss the consequences ofour results with respect to data collection strategies and methodologies.

Can co-location be used as a proxy for face-to-face contacts?