Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks

By Ciro Cattuto, Wouter Van den Broeck, Alain Barrat, Vittoria Colizza, Jean-François Pinton, Alessandro Vespignani. PLoS ONE, 2008

839 citations recorded by the source corpus.

Background Digital networks, mobile devices, and the possibility of mining the ever-increasing amount of digital traces that we leave behind in our daily activities are changing the way we can approach the study of human and social interactions. Large-scale datasets, however, are mostly available for collective and statistical behaviors, at coarse granularities, while high-resolution data on person-to-person interactions are generally limited to relatively small groups of individuals. Here we present a scalable experimental framework for gathering real-time data resolving face-to-face social interactions with tunable spatial and temporal granularities. Methods and Findings We use active Radio Frequency Identification (RFID) devices that assess mutual proximity in a distributed fashion by exchanging low-power radio packets. We analyze the dynamics of person-to-person interaction networks obtained in three high-resolution experiments carried out at different orders of magnitude in community size. The data sets exhibit common statistical properties and lack of a characteristic time scale from 20 seconds to several hours. The association between the number of connections and their duration shows an interesting super-linear behavior, which indicates the possibility of defining super-connectors both in the number and intensity of connections. Conclusions Taking advantage of scalability and resolution, this experimental framework allows the monitoring of social interactions, uncovering similarities in the way individuals interact in different contexts, and identifying patterns of super-connector behavior in the community. These results could impact our understanding of all phenomena driven by face-to-face interactions, such as the spreading of transmissible infectious diseases and information.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/34206c618908f528f412a7e71aae034ee2aaf77b

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Citations in the corpus

What's in a crowd? Analysis of face-to-face behavioral networks (2010, background): “These distributions are very broad (Cattuto et al., 2010): while most links correspond to very short contacts, some correspond to very long cumulated durations, and all time scales are represented, that is, no characteristic interaction timescale (except for obvious cutoffs) can be determined.”
Digital Epidemiology (2012, background): “Analogously, advances in wearable devices have radically improved our capability to track human contacts at high spatial and temporal resolution [22], affording a much more detailed characterization and understanding of social behaviors [23], complementing previous work based on large-scale surveys ”
COVID-19 lockdown induces disease-mitigating structural changes in mobility networks (2020, background): “Mirroring real systems, we can assume that this inequality holds when averaged over a single day, because (i) most individuals are isolated at night and (ii) many individuals are not in contact with other people for the majority of the day (14, 15).”
Projecting social contact matrices in 152 countries using contact surveys and demographic data (2017, background): “Triadic contacts-where A contacts B contacts C contacts A-are important for disease propagation in contact network models [12, 61] and other methods to measure these, such as radio frequency tagging [61, 62] , or more sophisticated structural models accounting temporal presence within the household ”
Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees (2011, methodology): “Our results bear also similarity with those reported previously for interaction networks at conferences [30,36], in which the resulting picture was not characterized by the presence of ‘superspreaders’, when they were defined in terms of the number of distinct individuals contacted.”
Contact Patterns among High School Students (2014, methodology): “As described in detail elsewhere [20,22,23,25], the power level is tuned so that devices can exchange packets only when located within 1-1.”
Temporal Networks (2011, background): “Importantly, there is an increasing body of evidence of very large heterogeneity in the timings of such interactions, from burstiness in patterns of communication via electronic and physical mail [10,11,17,18,114,115], mobile telephone calls and text messages [20,25–27], instant messaging [23], and ”
Coupled disease–behavior dynamics on complex networks: A review (2015, positive): “This can be characterized by measuring the statistical distribution of the duration per contact and the time intervals between successive contacts [163] .”
Contact Patterns in a High School: A Comparison between Data Collected Using Wearable Sensors, Contact Diaries and Friendship Surveys (2015, background): “Wearable sensors on the other hand can be tuned to specifically detect close-range face-to-face proximity [18, 20, 21].”
High-Resolution Measurements of Face-to-Face Contact Patterns in a Primary School (2011, methodology): “The infrastructure parameters are tuned so that the proximity of two individuals wearing the RFID badges can be assessed with a probability in excess of 99% over an interval of 20 seconds [15,16].”