Contact Patterns among High School Students

Abstract

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.

A mathematical programming approach for sequential clustering of dynamic networks

“…Figure 5 shows the evolution of the network community structure for ε = 0.15. Notice that during t 4 and t 5 the red and blue communities are merged, they correspond to the two MP classes in the network, showing that students are also more likely to interact with others in similar disciplines than with the rest as previously noted by [34]. In all test cases where ε > 0.05 and ε < 0.30, this pattern occurs to some extent at these time steps; it can also be perceived from Figure 5 where for these values of ε, there is drastic change in interaction patterns at t 4 and t 5 (drop in ARI) that is followed by an increase in the metric for the next time steps (t 6 and t 7 ).…”

“…The High School network represents the interactions among 180 high school students, over a period of 7 school days [34]. Students had to wear a device that would record any face to face contact with another student that lasted at least 20 s. The results were used to generate daily networks.…”

“…We take these classes as our ground truth communities and we investigate how closely our model detects them. Students are more likely to interact with other members of the same class [34].…”

Condensing Temporal Networks using Propagation

“…We also examined the super-times, and discovered that the days in and around the weekend (where there is little activity) were merged together. School: It is socio-contact network between high school students from five different sections over several days [5]. In G cond , we find a super-node containing nodes from MP*1 and MP*2 sections and another super-node with nodes from remaining three sections PC, PC*, and PSI.…”

Évaluer le risque épidémique à partir de données lacunaires

“…On obtient ainsi un enregistrement direct et objectif des contacts au sein du contexte considéré (école, lycée, hôpital, immeuble de bureaux, conférence, etc. [2,3]). La simulation de la propagation d'épidémies à partir de ces données permet ensuite d'améliorer la pré-diction du risque épidémique dans ces populations.…”