Contact Patterns among High School Students

被引:180
|
作者
Fournet, Julie [1 ]
Barrat, Alain [1 ,2 ]
机构
[1] Univ Toulon & Var, CNRS, CPT, Aix Marseille Univ,UMR 7332, Marseille, France
[2] ISI Fdn, Data Sci Lab, Turin, Italy
来源
PLOS ONE | 2014年 / 9卷 / 09期
关键词
DYNAMIC SOCIAL NETWORKS; INFECTIOUS-DISEASE; AIRBORNE INFECTIONS; MIXING PATTERNS; SPREAD; TRANSMISSION; PARAMETERS; EPIDEMICS; INFLUENZA; BEHAVIOR;
D O I
10.1371/journal.pone.0107878
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
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页数:17
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