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      Quantifying social contacts in a household setting of rural Kenya using wearable proximity sensors

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          Abstract

          Close proximity interactions between individuals influence how infections spread. Quantifying close contacts in developing world settings, where such data is sparse yet disease burden is high, can provide insights into the design of intervention strategies such as vaccination. Recent technological advances have enabled collection of time-resolved face-to-face human contact data using radio frequency proximity sensors. The acceptability and practicalities of using proximity devices within the developing country setting have not been investigated.

          We present and analyse data arising from a prospective study of 5 households in rural Kenya, followed through 3 consecutive days. Pre-study focus group discussions with key community groups were held. All residents of selected households carried wearable proximity sensors to collect data on their close (<1.5 metres) interactions. Data collection for residents of three of the 5 households was contemporaneous. Contact matrices and temporal networks for 75 individuals are defined and mixing patterns by age and time of day in household contacts determined. Our study demonstrates the stability of numbers and durations of contacts across days. The contact durations followed a broad distribution consistent with data from other settings. Contacts within households occur mainly among children and between children and adults, and are characterised by daily regular peaks in the morning, midday and evening. Inter-household contacts are between adults and more sporadic when measured over several days. Community feedback indicated privacy as a major concern especially regarding perceptions of non-participants, and that community acceptability required thorough explanation of study tools and procedures.

          Our results show for a low resource setting how wearable proximity sensors can be used to objectively collect high-resolution temporal data without direct supervision. The methodology appears acceptable in this population following adequate community engagement on study procedures. A target for future investigation is to determine the difference in contact networks within versus between households. We suggest that the results from this study may be used in the design of future studies using similar electronic devices targeting communities, including households and schools, in the developing world context.

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          The online version of this article (doi:10.1140/epjds/s13688-016-0084-2) contains supplementary material.

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          Most cited references52

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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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            Temporal Networks

            A great variety of systems in nature, society and technology -- from the web of sexual contacts to the Internet, from the nervous system to power grids -- can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks.
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              Role of social networks in shaping disease transmission during a community outbreak of 2009 H1N1 pandemic influenza.

              Evaluating the impact of different social networks on the spread of respiratory diseases has been limited by a lack of detailed data on transmission outside the household setting as well as appropriate statistical methods. Here, from data collected during a H1N1 pandemic (pdm) influenza outbreak that started in an elementary school and spread in a semirural community in Pennsylvania, we quantify how transmission of influenza is affected by social networks. We set up a transmission model for which parameters are estimated from the data via Markov chain Monte Carlo sampling. Sitting next to a case or being the playmate of a case did not significantly increase the risk of infection; but the structuring of the school into classes and grades strongly affected spread. There was evidence that boys were more likely to transmit influenza to other boys than to girls (and vice versa), which mimicked the observed assortative mixing among playmates. We also investigated the presence of abnormally high transmission occurring on specific days of the outbreak. Late closure of the school (i.e., when 27% of students already had symptoms) had no significant impact on spread. School-aged individuals (6-18 y) facilitated the introduction and spread of influenza in households, but only about one in five cases aged >18 y was infected by a school-aged household member. This analysis shows the extent to which clearly defined social networks affect influenza transmission, revealing strong between-place interactions with back-and-forth waves of transmission between the school, the community, and the household.
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                Author and article information

                Contributors
                ciro.cattuto@isi.it
                Journal
                EPJ Data Sci
                EPJ Data Sci
                Epj Data Science
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                2193-1127
                14 June 2016
                14 June 2016
                2016
                : 5
                : 21
                Affiliations
                [ ]KEMRI - Wellcome Trust Research Programme, Kilifi, Kenya
                [ ]Data Science Laboratory, ISI Foundation, Via Alassio 11/c, Torino, 10126 Italy
                [ ]School of Mathematics, The University of Manchester, Manchester, UK
                [ ]Bitmanufaktory Ltd, Cambridge, UK
                [ ]Aix-Marseille Université, Université de Toulon, CNRS, CPT, UMR 7332, Marseille, 13288 France
                [ ]School of Life Sciences and WIDER, University of Warwick, Coventry, UK
                Author information
                http://orcid.org/0000-0001-9526-4364
                Article
                84
                10.1140/epjds/s13688-016-0084-2
                4944592
                27471661
                07444e1f-c683-4ee5-bf92-b90fc79bc66c
                © Kiti et al. 2016

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 27 November 2015
                : 6 June 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 084633
                Award ID: 098556
                Award ID: 102975
                Award ID: 077092
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004963, Seventh Framework Programme (BE);
                Award ID: 317532
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100007364, Fondazione CRT;
                Award ID: Lagrange Project
                Award Recipient :
                Categories
                Regular Article
                Custom metadata
                © The Author(s) 2016

                households,contact patterns,contact networks,wearable proximity sensors,respiratory infections,infectious disease control

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