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

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          Abstract

          Background

          Little 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 Findings

          Data on face-to-face interactions were collected on Thursday, October 1 st and Friday, October 2 nd 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.

          Conclusions

          We 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.

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          Most cited references24

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          Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks

          Background Digital networks, mobile devices, and the possibility of mining the ever-increasing amount of digital traces that we leave behind in our daily activities are changing the way we can approach the study of human and social interactions. Large-scale datasets, however, are mostly available for collective and statistical behaviors, at coarse granularities, while high-resolution data on person-to-person interactions are generally limited to relatively small groups of individuals. Here we present a scalable experimental framework for gathering real-time data resolving face-to-face social interactions with tunable spatial and temporal granularities. Methods and Findings We use active Radio Frequency Identification (RFID) devices that assess mutual proximity in a distributed fashion by exchanging low-power radio packets. We analyze the dynamics of person-to-person interaction networks obtained in three high-resolution experiments carried out at different orders of magnitude in community size. The data sets exhibit common statistical properties and lack of a characteristic time scale from 20 seconds to several hours. The association between the number of connections and their duration shows an interesting super-linear behavior, which indicates the possibility of defining super-connectors both in the number and intensity of connections. Conclusions Taking advantage of scalability and resolution, this experimental framework allows the monitoring of social interactions, uncovering similarities in the way individuals interact in different contexts, and identifying patterns of super-connector behavior in the community. These results could impact our understanding of all phenomena driven by face-to-face interactions, such as the spreading of transmissible infectious diseases and information.
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            Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents.

            The estimation of transmission parameters has been problematic for diseases that rely predominantly on transmission of pathogens from person to person through small infectious droplets. Age-specific transmission parameters determine how such respiratory agents will spread among different age groups in a human population. Estimating the values of these parameters is essential in planning an effective response to potentially devastating pandemics of smallpox or influenza and in designing control strategies for diseases such as measles or mumps. In this study, the authors estimated age-specific transmission parameters by augmenting infectious disease data with auxiliary data on self-reported numbers of conversational partners per person. They show that models that use transmission parameters based on these self-reported social contacts are better able to capture the observed patterns of infection of endemically circulating mumps, as well as observed patterns of spread of pandemic influenza. The estimated age-specific transmission parameters suggested that school-aged children and young adults will experience the highest incidence of infection and will contribute most to further spread of infections during the initial phase of an emerging respiratory-spread epidemic in a completely susceptible population. These findings have important implications for controlling future outbreaks of novel respiratory-spread infectious agents.
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              Risk factors of influenza transmission in households.

              Influenza transmission in households is a subject of renewed interest, as the vaccination of children is currently under debate and antiviral treatments have been approved for prophylactic use. To quantify the risk factors of influenza transmission in households. A prospective study conducted during the 1999 to 2000 winter season in France. Nine hundred and forty-six households where a member, the index patient, had visited their general practitioner (GP) because of an influenza-like illness were enrolled in the study. Five hundred and ten of the index patients tested positive for influenza A (subtype H3N2). A standardised daily questionnaire allowed for identification of secondary cases of influenza among their household contacts, who were followed-up for 15 days. Of the 395 (77%) households that completed the questionnaire, we selected 279 where no additional cases had occurred on the day of the index patient's visit to the GP. Secondary cases of influenza were those household contacts who had developed clinical influenza within 5 days of the disease onset in the index patient. Hazard ratios for individual clinical and demographic characteristics of the contact and their index patient were derived from a Cox regression model. Overall in the 279 households, 131 (24.1%) secondary cases occurred among the 543 household contacts. There was an increased risk of influenza transmission in preschool contacts (hazard ratio [HR] = 1.85, 95% confidence interval [CI] = 1.09 to 3.26) as compared with school-age and adult contacts. There was also an increased risk in contacts exposed to preschool index patients (HR = 1.93, 95% CI = 1.09 to 3.42) and school-age index patients (HR = 1.68, 95% CI = 1.07 to 2.65), compared with those exposed to adult index cases. No other factor was associated with transmission of the disease. Our results support the major role of children in the dissemination of influenza in households. Vaccination of children or prophylaxis with neuraminidase inhibitors would prevent, respectively, 32-38% and 21-41% of secondary cases caused by exposure to a sick child in the household.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2011
                16 August 2011
                : 6
                : 8
                : e23176
                Affiliations
                [1 ]Centre de Physique Théorique de Marseille, CNRS UMR 6207, Marseille, France
                [2 ]Hospices Civils de Lyon, Hôpital Edouard Herriot, Service d'Hygiène, Epidémiologie et Prévention, Lyon, France
                [3 ]Université de Lyon, Université Lyon 1, CNRS UMR 5558, Laboratoire de Biométrie et de Biologie Evolutive, Equipe Epidémiologie et Santé Publique, Lyon, France
                [4 ]Data Science Laboratory, Institute for Scientific Interchange (ISI) Foundation, Torino, Italy
                [5 ]Laboratoire de Physique de l'Ecole Normale Supérieure de Lyon, CNRS UMR 5672, Lyon, France
                [6 ]Hospices Civils de Lyon, National Influenza Centre, Laboratory of Virology, Lyon, France
                [7 ]VIRPATH, CNRS FRE 3011, UCBL, Université de Lyon, Faculté de Médecine RTH Laennec, Lyon, France
                National Institutes of Health, United States of America
                Author notes

                Conceived and designed the experiments: AB CC LI BL J-FP JS WVdB PV NV. Performed the experiments: AB CC J-FP CR NV. Analyzed the data: AB CC LI MQ JS WVdB NV. Wrote the paper: AB CC J-FP JS NV.

                Article
                PONE-D-11-06413
                10.1371/journal.pone.0023176
                3156713
                21858018
                09ff3832-da48-4ea3-b13f-957fa33b32f3
                Stehlé et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 8 April 2011
                : 7 July 2011
                Page count
                Pages: 13
                Categories
                Research Article
                Medicine
                Epidemiology
                Infectious Disease Epidemiology
                Social Epidemiology
                Infectious Diseases
                Infectious Disease Control
                Infectious Disease Modeling
                Physics
                Interdisciplinary Physics
                Statistical Mechanics
                Social and Behavioral Sciences
                Sociology
                Computational Sociology
                Social Networks
                Social Systems

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                Uncategorized

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