Abstract
Technological advances have led to a strong increase in the number of data collection efforts aimed at measuring co-presence of individuals at different spatial resolutions. It is however unclear how much co-presence data can inform us on actual face-to-face contacts, of particular interest to study the structure of a population in social groups or for use in data-driven models of information or epidemic spreading processes. Here, we address this issue by leveraging data sets containing high resolution face-to-face contacts as well as a coarser spatial localisation of individuals, both temporally resolved, in various contexts. The co-presence and the face-to-face contact temporal networks share a number of structural and statistical features, but the former is (by definition) much denser than the latter. We thus consider several down-sampling methods that generate surrogate contact networks from the co-presence signal and compare them with the real face-to-face data. We show that these surrogate networks reproduce some features of the real data but are only partially able to identify the most central nodes of the face-to-face network. We then address the issue of using such down-sampled co-presence data in data-driven simulations of epidemic processes, and in identifying efficient containment strategies. We show that the performance of the various sampling methods strongly varies depending on context. We discuss the consequences of our results with respect to data collection strategies and methodologies.
Highlights
In the recent years, several methods have been developed to gather quantitative data on human interactions using wearable sensors and complement more traditional methods based on surveys [1,2,3]
5 Discussion and conclusion In this paper, we have investigated whether low resolution co-presence information can be used as a substitute for detailed face-to-face proximity data, both from the point of view of extracting large-scale structural and statistical features of the temporal contact
We have considered several data sets collected in various contexts that contain both high-resolution data on face-to-face contacts between individuals and a coarser location data, both with temporal resolution
Summary
Several methods have been developed to gather quantitative data on human interactions using wearable sensors and complement more traditional methods based on surveys [1,2,3]. Since gathering large-scale data about localisation is much easier than recording face-to-face contacts, a method to infer general characteristics of the latter from the former would enable faster, larger and more diverse data collections about human behaviour. To this aim, we leverage several data sets collected by the SocioPatterns collaboration [13, 27] in various contexts: these data include both detailed information about close, face-to-face encounters between individuals and a location tracking of individuals with low spatial resolution. Our results turn out to depend strongly on the data collection context, highlighting the limitations of coarse co-presence networks with respect to detailed face-to-face data
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