We increasingly use digital media and computational devices in our daily activities, and leave behind a sizable amount of digital traces while doing so. The proliferation of mobile devices, and the incorporation of various sensing technologies in these devices, will further add to this growing trail of data. The possibility to mine and analyze these data, and the scale at which this can be done on contemporary computer systems, affords a novel, data-driven approach in the investigation of various aspects of human behavior.

SocioPatterns is an interdisciplinary research collaboration that adopts this data-driven methodology with the aim of uncovering fundamental patterns in social dynamics and coordinated human activity.

To achieve its scientific goals, the SocioPatterns collaboration also contributes to the development of new technologies for collecting relevant data. In particular, the collaboration supports the development of the SocioPatterns sensing platform, which uses wireless wearable sensors to gather longitudinal data on human mobility and  face-to-face proximity in real-world environments.  The SocioPatterns team also works on developing tools and techniques to represent, analyze and visualize the collected data.


Composite image. Left: One of the sixty-nine daily diagrams of contact activity. Right: Thumbnail of the poster with the complete visualization and accompanying text.

Left: One of the sixty-nine daily diagrams of contact activity. Right: Thumbnail of the poster with the complete visualization and accompanying text.

We have created a visualization of sixty-nine days of face-to-face contact activity among more that 30,000 persons based on data collected during the INFECTIOUS: STAY AWAY exhibition in the Science Gallery in Dublin, Ireland. This visualization is published in our gallery as a poster that can be freely downloaded.



SocioPatterns is a collaboration between researchers and developers from the following institutions and companies:


New paper in Nature Communications

We have published a new paper in Nature Communications. In this paper, we consider the issue of how to correctly inform numerical models of the propagation of infectious diseases when only partial information on the contacts of individuals is available, due to population sampling.
Indeed, the coverage of the population in many measures of detailed contact networks is incomplete, and this yields a systematic underestimation of epidemic risk if the data is used without precaution. Here, we introduce a method to compensate for this systematic bias and obtain accurate evaluations of epidemic risk using incomplete data. To this aim, we have developed an algorithm that effectively fills in the gaps of the empirical data with a realistic picture of the missing contact network. Although the obtained surrogate contacts are different from the actual missing contacts, using them in the simulation of an influenza-like process gives an accurate estimation of what would have been obtained on using complete data. It is therefore possible to have a good estimation of the epidemic risk, even if a substantial fraction of the contacts are missing from the empirical data.

New paper and new data available!

We have just published a new paper in PLoS ONE. In this paper, we present a detailed comparison between various types of data describing contacts and relationships between students in a high school: data collected from wearable sensors, data from contact diaries and data from surveys in which students were asked to name their friends.

We release all the corresponding data both in the Supplementary Information of the paper and in the SocioPatterns page dedicated to data.


SocioPatterns at a full-scale emergency response exercise

On May 27t the SocioPatterns platform was deployed to track and analyze the interactions of people, objects and spaces during a full-scale exercise organized by the CRIMEDIM Research Center in Emergency and Disaster Medicine in collaboration with the Italian Army and a number of other partners, including the ISI Foundation. The exercise involved almost 500 people, a ROLE2+ military field hospital, 2 primary care centers, 8 ambulances and a coordination center. The tracking system featured fully-distributed recording of the interactions between people, ambulances, hospital rooms and equipment, with real-time monitoring of the hospital workflow and live views in the operations center.



ISI Foundation logo  CNRS logo