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.
FEATURED: INFECTIOUS SOCIOPATTERNS POSTER
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:
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.
We have just published a new paper in BMC Infectious Diseases where we compare different strategies for epidemic mitigation in a primary school and investigate the performance of targeted class-closure interventions.
In the Supplementary Information of the paper, we release a dataset on the face-to-face interactions in a primary school, measured by using wearable proximity sensors. The dataset covers over 200 persons (students and teachers) for about 2 days, and provides not only a complex temporal network, but also the ground truth for communities, i.e., a school class attribute for all nodes in the network. Enjoy!
We have launched a pilot study in collaboration with Laura Ozella and her team at the University of Torino, Italy. We aim at instrumenting colonies of feral cats in the city of Torino with new proximity sensors programmed by the ISI Data Science Lab to operate in a fully distributed fashion. We will measure the social structure of feral cat colonies in urban settings, and put these data in relation to clinical and microbiological data on cats’ health and transmissible conditions. This pilot study is part of a broader study on animal health and environmental risk factors led by Laura Ozella.