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Contact Patterns among High School Students

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  • Julie Fournet
  • Alain Barrat

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.

Suggested Citation

  • Julie Fournet & Alain Barrat, 2014. "Contact Patterns among High School Students," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-17, September.
  • Handle: RePEc:plo:pone00:0107878
    DOI: 10.1371/journal.pone.0107878
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    References listed on IDEAS

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    1. Joël Mossong & Niel Hens & Mark Jit & Philippe Beutels & Kari Auranen & Rafael Mikolajczyk & Marco Massari & Stefania Salmaso & Gianpaolo Scalia Tomba & Jacco Wallinga & Janneke Heijne & Malgorzata Sa, 2008. "Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 5(3), pages 1-1, March.
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    Cited by:

    1. Li, Mingwu & Dankowicz, Harry, 2019. "Impact of temporal network structures on the speed of consensus formation in opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1355-1370.
    2. Mitja Steinbacher & Matthias Raddant & Fariba Karimi & Eva Camacho Cuena & Simone Alfarano & Giulia Iori & Thomas Lux, 2021. "Advances in the agent-based modeling of economic and social behavior," SN Business & Economics, Springer, vol. 1(7), pages 1-24, July.
    3. Bi, Jialin & Jin, Ji & Qu, Cunquan & Zhan, Xiuxiu & Wang, Guanghui & Yan, Guiying, 2021. "Temporal gravity model for important node identification in temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    4. Liu, Kang & Yin, Ling & Ma, Zhanwu & Zhang, Fan & Zhao, Juanjuan, 2020. "Investigating physical encounters of individuals in urban metro systems with large-scale smart card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    5. Rauf Ahmed Shams Malick & Syed Kashir Hasan & Fahad Samad & Nadeem Kafi Khan & Hassan Jamil Syed, 2023. "Smart Methods to Deal with COVID-19 at University-Level Institutions Using Social Network Analysis Techniques," Sustainability, MDPI, vol. 15(6), pages 1-17, March.
    6. Mattia Mazzoli & Riccardo Gallotti & Filippo Privitera & Pere Colet & José J. Ramasco, 2023. "Spatial immunization to abate disease spreading in transportation hubs," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    7. Funel, Agostino, 2022. "A method to compute the communicability of nodes through causal paths in temporal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    8. Aming Li & Yang-Yu Liu, 2020. "Controlling Network Dynamics," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(07n08), pages 1-19, February.
    9. Gail E. Potter & Nicole Bohme Carnegie & Jonathan D. Sugimoto & Aldiouma Diallo & John C. Victor & Kathleen M. Neuzil & M. Elizabeth Halloran, 2022. "Using social contact data to improve the overall effect estimate of a cluster‐randomized influenza vaccination program in Senegal," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 70-90, January.

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