Contact Patterns among High School Students

By Julie Fournet, Alain Barrat. PLoS ONE, 2014

324 citations recorded by the source corpus.

Face-to-face contacts between individuals contribute to shape social networks and play an important role in determining how infectious diseases can spread within a population. It is thus important to obtain accurate and reliable descriptions of human contact patterns occurring in various day-to-day life contexts. Recent technological advances and the development of wearable sensors able to sense proximity patterns have made it possible to gather data giving access to time-varying contact networks of individuals in specific environments. Here we present and analyze two such data sets describing with high temporal resolution the contact patterns of students in a high school. We define contact matrices describing the contact patterns between students of different classes and show the importance of the class structure. We take advantage of the fact that the two data sets were collected in the same setting during several days in two successive years to perform a longitudinal analysis on two very different timescales. We show the high stability of the contact patterns across days and across years: the statistical distributions of numbers and durations of contacts are the same in different periods, and we observe a very high similarity of the contact matrices measured in different days or different years. The rate of change of the contacts of each individual from one day to the next is also similar in different years. We discuss the interest of the present analysis and data sets for various fields, including in social sciences in order to better understand and model human behavior and interactions in different contexts, and in epidemiology in order to inform models describing the spread of infectious diseases and design targeted containment strategies.

Subfields: Epidemiology, Modeling and Simulation, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/981e9e108c5f6316e84c8bf38101c6222e82103e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Epidemiology

In Modeling and Simulation

In Statistical and Nonlinear Physics

Citations in the corpus

The fundamental advantages of temporal networks (2016, background): “This requires that all derivatives of B T e A T ( t f − s ) x equal to 0 at t f , that is Student contacts : This data set consists of a sequence of contacts between students in a high school in Marseilles, France, with 126 students in three classes over 4 days in Dec. 2011 [39].”
Understanding Human Mobility from Twitter (2014, background): “Recent effort has focused on the study of human mobility using new tracking technologies such as mobile phones [7–10], GPS [11–13], Wifi [14, 15], and RFID devices [16, 17].”
Contact Patterns in a High School: A Comparison between Data Collected Using Wearable Sensors, Contact Diaries and Friendship Surveys (2015, background): “The active involvment of some students ensured a good participation of other students to the data collection, as also reported in other works [9, 24].”
Statistical physics of vaccination (2016, background): “Interestingly, the same holds considering different years (for different students) [708].”