High-Resolution Measurements of Face-to-Face Contact Patterns in a Primary School

Abstract

BackgroundLittle quantitative information is available on the mixing patterns of children in school environments. Describing and understanding contacts between children at school would help quantify the transmission opportunities of respiratory infections and identify situations within schools where the risk of transmission is higher. We report on measurements carried out in a French school (6–12 years children), where we collected data on the time-resolved face-to-face proximity of children and teachers using a proximity-sensing infrastructure based on radio frequency identification devices.Methods and FindingsData on face-to-face interactions were collected on Thursday, October 1st and Friday, October 2nd 2009. We recorded 77,602 contact events between 242 individuals (232 children and 10 teachers). In this setting, each child has on average 323 contacts per day with 47 other children, leading to an average daily interaction time of 176 minutes. Most contacts are brief, but long contacts are also observed. Contacts occur mostly within each class, and each child spends on average three times more time in contact with classmates than with children of other classes. We describe the temporal evolution of the contact network and the trajectories followed by the children in the school, which constrain the contact patterns. We determine an exposure matrix aimed at informing mathematical models. This matrix exhibits a class and age structure which is very different from the homogeneous mixing hypothesis.ConclusionsWe report on important properties of the contact patterns between school children that are relevant for modeling the propagation of diseases and for evaluating control measures. We discuss public health implications related to the management of schools in case of epidemics and pandemics. Our results can help define a prioritization of control measures based on preventive measures, case isolation, classes and school closures, that could reduce the disruption to education during epidemics.

Temporal networks

“…Here, the pioneering study was the Reality Mining project, where students of Massachusetts Institute of Technology were equipped with cell phones whose Bluetooth devices could detect their proximity to others [37]. The SocioPatterns project has developed a platform that allows physical proximity measurements based on wearable badges equipped with radiofrequency identification devices (RFID) [24]; these devices have been utilized in measurements of dynamic and temporal proximity networks of patients [63], school children [142], and conference attendees [140]. Because the human body acts as a shield for the proximity-sensing RF signals, such sensors only record contacts when the individuals are facing each other, and thus a contact can also be considered as indicative of communication between the individuals [24,64,121,141].…”

Face-to-Face Contacts at a Conference: Dynamics of Communities and Roles

“…They analyze, for example, their activity in social web platforms like Facebook, Twitter and other social media together with status and their research seniority. These experiments have also extended their focus from conferences to schools [10] and hospitals [11].…”

Backtracking and Mixing Rate of Diffusion on Uncorrelated Temporal Networks

“…As a next step, we test the importance of this effect in real-world systems by considering four datasets of face-to-face contacts described in [21][22][23][24][25]. From the recorded contacts, we obtain the largest component with a typical size of a few hundred nodes.…”