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:
We have just published a new paper in BMC Infectious Diseases. We use SocioPatterns data collected in a hospital ward to ask which representations of contact data work best to inform models of disease spread. We show that the commonly used contact matrix representation fails to reproduce the size of the epidemic obtained using the high-resolution contact data and also fails to identify the most at-risk classes. We introduce a contact matrix of probability distributions that takes into account the heterogeneity of contact durations between (and within) classes of individuals, and we show that, in the case study presented, this representation yields a good approximation of the epidemic spreading properties obtained by using the high-resolution data.
A new manuscript describing research done using SocioPatterns data collected in two jointly organized conferences is available here.