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      Estimates of Social Contact in a Middle School Based on Self-Report and Wireless Sensor Data

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          Abstract

          Estimates of contact among children, used for infectious disease transmission models and understanding social patterns, historically rely on self-report logs. Recently, wireless sensor technology has enabled objective measurement of proximal contact and comparison of data from the two methods. These are mostly small-scale studies, and knowledge gaps remain in understanding contact and mixing patterns and also in the advantages and disadvantages of data collection methods. We collected contact data from a middle school, with 7th and 8th grades, for one day using self-report contact logs and wireless sensors. The data were linked for students with unique initials, gender, and grade within the school. This paper presents the results of a comparison of two approaches to characterize school contact networks, wireless proximity sensors and self-report logs. Accounting for incomplete capture and lack of participation, we estimate that “sensor-detectable”, proximal contacts longer than 20 seconds during lunch and class-time occurred at 2 fold higher frequency than “self-reportable” talk/touch contacts. Overall, 55% of estimated talk-touch contacts were also sensor-detectable whereas only 15% of estimated sensor-detectable contacts were also talk-touch. Contacts detected by sensors and also in self-report logs had longer mean duration than contacts detected only by sensors (6.3 vs 2.4 minutes). During both lunch and class-time, sensor-detectable contacts demonstrated substantially less gender and grade assortativity than talk-touch contacts. Hallway contacts, which were ascertainable only by proximity sensors, were characterized by extremely high degree and short duration. We conclude that the use of wireless sensors and self-report logs provide complementary insight on in-school mixing patterns and contact frequency.

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          Most cited references24

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

            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 1st and Friday, October 2nd 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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              Modeling targeted layered containment of an influenza pandemic in the United States.

              Planning a response to an outbreak of a pandemic strain of influenza is a high public health priority. Three research groups using different individual-based, stochastic simulation models have examined the consequences of intervention strategies chosen in consultation with U.S. public health workers. The first goal is to simulate the effectiveness of a set of potentially feasible intervention strategies. Combinations called targeted layered containment (TLC) of influenza antiviral treatment and prophylaxis and nonpharmaceutical interventions of quarantine, isolation, school closure, community social distancing, and workplace social distancing are considered. The second goal is to examine the robustness of the results to model assumptions. The comparisons focus on a pandemic outbreak in a population similar to that of Chicago, with approximately 8.6 million people. The simulations suggest that at the expected transmissibility of a pandemic strain, timely implementation of a combination of targeted household antiviral prophylaxis, and social distancing measures could substantially lower the illness attack rate before a highly efficacious vaccine could become available. Timely initiation of measures and school closure play important roles. Because of the current lack of data on which to base such models, further field research is recommended to learn more about the sources of transmission and the effectiveness of social distancing measures in reducing influenza transmission.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                21 April 2016
                2016
                : 11
                : 4
                : e0153690
                Affiliations
                [1 ]Department of Internal Medicine, University of Utah, Salt Lake City, Utah, United States of America
                [2 ]Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, United States of America
                [3 ]Department of Mathematics, University of Utah, Salt Lake City, Utah, United States of America
                [4 ]Department of Global Migration and Quarantine, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
                [5 ]Department of Biomedical Informatics, University of Utah, Salt Lake City, United States of America
                Melbourne School of Population Health, AUSTRALIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: ML DT WP JR HG AU MS. Performed the experiments: ML DT WP. Analyzed the data: ML DT WP. Contributed reagents/materials/analysis tools: ML DT WP. Wrote the paper: ML DT WP JR HG AU MS.

                Article
                PONE-D-15-39161
                10.1371/journal.pone.0153690
                4839567
                27100090
                4ef75b24-10f5-4cac-8e70-5de8987ae6f9

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 4 September 2015
                : 3 April 2016
                Page count
                Figures: 7, Tables: 6, Pages: 21
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100006088, National Center for Emerging and Zoonotic Infectious Diseases;
                Award ID: 1U01CK000177-01
                Award Recipient :
                This study was funded by Centers for Disease Control and Prevention cooperative agreement 1U01CK000177-01 to MS and ML. The authors, JR, HG, and AU, from the funding organization participated in study design and preparation of the manuscript. The findings and conclusions in this study are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
                Categories
                Research Article
                Social Sciences
                Sociology
                Education
                Schools
                Medicine and Health Sciences
                Epidemiology
                Infectious Disease Epidemiology
                Medicine and Health Sciences
                Infectious Diseases
                Infectious Disease Epidemiology
                Medicine and Health Sciences
                Pulmonology
                Respiratory Infections
                Engineering and Technology
                Signal Processing
                Medicine and Health Sciences
                Infectious Diseases
                Viral Diseases
                Influenza
                Medicine and Health Sciences
                Infectious Diseases
                Infectious Disease Modeling
                Biology and Life Sciences
                Computational Biology
                Population Modeling
                Infectious Disease Modeling
                Biology and Life Sciences
                Population Biology
                Population Modeling
                Infectious Disease Modeling
                Computer and Information Sciences
                Network Analysis
                Computer and Information Sciences
                Information Technology
                Data Processing
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

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