Since the beginning of the past week, we have been running a pilot experiment in a major Italian pediatric hospital, in a collaboration with the hospital and the ISS. The experiment involves patients, doctors, healthcare workers and visitors, simultaneously collecting contact information and clinical information. We aim at measuring epidemiologically relevant quantities in a real-world healthcare setting.
Posts tagged ‘experiment’
First deployment in a healthcare setting
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Announcing a new experiment in a primary school
On October 1-2 a SocioPatterns experiment will run in a new setting: a primary school in Lyon, France. We will record the proximity patterns of about 240 students.
Live Social Semantics
In moving from measuring social interactions to augmenting them, a critical task is the integration of heterogeneous data sources, such as real-world face-to-face contacts, on-line friendships and shared interests (both explicitly stated or implicitly inferred from metadata). This integration holds the key to exposing the social semantics of the measured interactions, and paves the door to new kinds of applications that assist social networking, support social browsing, and provide recomendation, and serendipitous discovery.
We believe that the best way to learn about the right research questions is to actually design and build a working system that can be deployed at real-life social gatherings. We partnered with the TAGora project, and specifically with the team of the University of Southampton (Harith Alani, Martin Szomszor and Gianluca Correndo) and designed the Live Social Semantics (LSS) experiment. The basic idea is to focus on a conference gathering and establish a contact between: A) the real-world identities and physical-space relations of conference attendees, B) their identities and relations in web-based systems for social networking and collaborative tagging, and C) their identities in semantic representation of knowledge that describe their interests and collaborations (paper co-authorship, for example). The SocioPatterns project provides the social sensing platform needed for A), while the TAGora project provided the profile-mining and semantic integration techniques required to collect, filter and represent B) and C). The movie below illustrates the experiment concept and the user-facing aspects of the system.
Attendees enroll in Live Social Semantics (LSS) by volunteering to wear a RFID-equipped badge that can detect face-fo-face proximity. This is the real-world part of the experiment. The real-world identity of an attendee is then associated with multiple on-line identities of her choice by creating a user profile in the LSS web interface. LSS currentlty supports Facebook, Flickr, Delicious, and Last.FM. Once a profile has been created, the system will gather data from these systems in the background, record on-line friendships and infer interests and shared interests. The system will also mine for co-membership in communities of practice (COP) using semantic web sources such as the RKBExplorer.
The data collected and integrated by LSS are stored as RDF relations in a triple store, and fed back to the conference attendees in a number of ways. There are public real-time visualizations of the ongoing face-to-face contacts (as in previous SocioPatterns experiments), but now annotated using information and profile pictures from on-line social networks and semantic data. Participants can also visit their account page on the LSS web interface and browse the list of persons they have been in contact with during the event, ranked by the measured strengths of the interactions. On top of that, the system provides an interactive web-based visualization that allows users to browse their ego networks across all supported systems, exploring the interplay of face-to-face time, on-line friendships and shared interest. The system also provides simple forms of recommendation, by suggesting the closure of social triangles that span the supported networks: for example, if attendee A has spent face-to-face time with attendee B, the system can point her to the profile of a third attendee C who is a Facebook friend of both A and B, and hasn’t met A yet at the event.
The architecture of the system and some basic usage statistics are reported in a paper that will be presented in the “Semantic Web in Use” track of the International Semantic Web Conference 2009 (ISWC2009). From a research perspective, the collected data enable to relate, quantitatively, how much an on-line friendship between individuals is predictive of their face-to-face time, and how the structure of the on-line and real-world social networks relate to one another. Results about this will be posted here in the coming weeks.
Contact duration
We just completed a first experiment with our new contact-detection firmware for the OpenBeacon platform. The experiment was hosted at a conference on “Facing the Challenge of Infectious Diseases” and involved about 50 attendees over four days. The new firmware proved to be as much reliable in a real-world setting as it appeared to be in our preliminary experiments. In the next posts, we will report on different aspects of this new experiment.
As a first example of hidden patterns in the dynamics of social contacts, we display below the probability of observing a face-to-face contact of a given duration. The probability P(Dt) of observing a contact of duration Dt is shown as a function of the contact duration Dt. The probability distribution was computed using a few hours of data. The interval of time we analyzed was divided into slices of 20 seconds, and for each of these slices the contact graph of the attendees was computed and used to assess when a new contact was established, continued, or broken. As a consequence, the contact durations we measure are multiples of the interval used for temporal coarse-graining (i.e., contacts last 20 seconds, 40 s, 60 s, …)
Remarkably, this simple analysis exposes a clear pattern of social contact: the duration of face-to-face contacts is power-law distributed (straight line on a log-log plot) with an exponent close to -2 (black line). This behavior holds over different periods of time, and is robust across different groups of individuals.
Qualitatively, the figure above shows something obvious: there are comparatively few long-lasting contacts and a multitude of brief contacts. It is remarkable, however, that such a clear-cut pattern emerges when observing a few tens of persons over a few hours. Moreover, the specific value of the scaling exponent (-2) shows that the duration of social contact is scale-free, i.e., there is no “typical” duration for contacts. While one can always compute an average duration of contacts over a finite sample of data, the scaling properties are such that the computed average will increase without limit on increasing the length of the experiment.
We close by noticing that we are not the first to observe this regularity for the duration of social contacts (see for example the work by Scherrer et al. on “Description and simulation of dynamic mobility networks”). Our measurements, however, achieve higher spatial and temporal accuracy than previous studies, and reliably select face-to-face interactions at close range, which are a very good proxy for social interaction.
Exposing contact patterns
SocioPatterns.org aims to shed light on hidden patterns in social dynamics. A case in point is the study of contact patterns, which deals with such patterns in contacts among people. To date, little is known about these patterns. Although models can help in learning more, measuring real-world dynamics is indispensable for obtaining a complete picture. However, doing so manually is both laborious and intrusive, and tends to produce unreliable data - yet until recently it was the sole option we had. Fortunately, emerging technologies such as active RFID devices offer previously unfeasible means for collecting this much needed data. While collecting data is the first step, making sense of the resulting large amounts of data is the next. This is where insightful visualizations come into play, as these can expose otherwise invisible features and regularities.
The following movie gives an impression of a first contact patterns experiment and visualization we have been working on. It is followed by a detailed description.
Experiment set-up
We have been working on an experimental set-up in which we aim to measure the contact patterns of a group of people. To do so we asked volunteers to wear small tags with integrated active RFID technology, henceforth called the beacons. These beacons continuously broadcast small data packets. These packets are received by a number of stations and relayed through a local network to a server for further processing. The stations are installed in fixed locations in the environment. The beacons and stations we used were created by and obtained from OpenBeacon.org.
Schematic overview of the interactions between the beacons, the stations, and the server.
A first medium-sized test deployment of this experimental set-up took place recently (26-29 May, 2008) during the workshop “Sociophysics: status and perspectives” in Villa Gualino, in Turin, Italy. The workshop presentations took place in Sala A, while the participants lingered in the Bar during breaks, and had lunch in the Cafeteria. All three areas were covered by at least four stations each, as shown on the map below.
Map of the placement of the stations in Villa Gualino.
The packets from up to 50 beacons were collected continuously for about 75 hours at an overall rate of up to 100 distinct packets per second. In total about 25 million packets were collected, corresponding to about 200 Mb of compressed raw data.
On-line data analysis and visualization server
In addition to recording data for further analysis - whose results will be posted here in the future - we also developed a real-time visualization system to display some aspects of the observed dynamics. Our visualization involves a server component and a client component. The server component processes the beacon packets relayed by the stations. These packets are typically received by multiple stations at varying strengths. These reception patterns can be used as a proxy for the physical proximity of two beacons. A related technique is used in the geo-location feature of the Apple iPod Touch and iPhone, or in services like PlaceEngine.
All calculations are performed in real-time over a sliding window of about two minutes. The post-processed data are dispatched to the visualization client at regular intervals, as an XML stream. The on-line data collection and analysis system is entirely coded in Python on top of the Twisted framework and the Numpy library.
Visualization client
The main visualization represents the beacons, the stations, and their relations of proximity as measured by the system. The beacons are shown as simple discs, which are optionally labeled. Two beacons are connected by a link if the system detected that they are close to each other. The length, thickness and transparency of a link are a function of the strength of the link: short, thick and more opaque links represent strong proximity; thin, transparent links indicate weak proximity. The size of the discs representing the beacons depends on the number and proximity of other beacons, and specifically is a function of the sum of link weights to other beacons. The stations are shown as labeled shapes and laid out in a circle that spans the main view. The size of these shapes varies according to the number of beacons that are close to them.
Stations and beacons in the main visualization view.
While the stations are laid out at fixed positions, the beacons are not. The network of their proximity relations is laid out by using a force-based model. Beacons repel each other, and the link between two beacons acts as a spring pulling them close to each other. The stiffness of the spring increases with the proximity of beacons. Beacons are also pulled towards the stations that see them more often, so that groups of nearby beacons are laid out in the vicinity of the stations that is closest to them.
The result is a first attempt to obtain a rather abstract yet comprehensive visualization of the proximity dynamics. The movie at the top of this post contains a time-lapse playback of the most interesting periods of one day of the workshop.
Visualization client interface
The visualization client is an Adobe Air application developed in Flex. The visualization and physics system use (a mildly modified version of) the flare visualization toolkit.
The application interface involves a visualization window, a control panel and a number of auxiliary windows. The visualization itself can be shown full-screen during presentations. The control panel provides various interfaces to manage the data-sources, the visualization and the physics engine.
The data source is either the server component for real-time visualizations, a simulation for off-line testing, or data files for time-lapse playback. When using a simulation, an additional window is provided in which the simulated stations and beacons are shown. When in time-lapse mode, one can activate the recorder, which saves a bitmap file for each rendered frame. These saved frames were for example used to create the movie at the top of this post.
The complete interface of the visualization client, including the simulation window.
Credits
visualisation: Wouter Van den Broeck
data analysis: Ciro Cattuto
music: Maps and Diagrams - Siaptik - Recorded by Tim Martin
experiment set-up: Alain Barrat, Ciro Cattuto, Vittoria Colizza, Daniela Paolotti, Jean-François Pinton, Wouter Van den Broeck, and Alessandro Vespignani
thanks to: Santo Fortunato, ISI administration, Ezio Borzani, Milosch Meriac (openbeacon.org), and the workshop participants
sponsoring institutions: Institute For Scientific Interchange Foundation (Torino, Italy), Laboratoire de Physique de l’École Normale Supérieure de Lyon (Lyon, France)
