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      Projecting social contact matrices in 152 countries using contact surveys and demographic data

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      1 , 1 , 2 , 3 , * , 4 , 5
      PLoS Computational Biology
      Public Library of Science

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          Abstract

          Heterogeneities in contact networks have a major effect in determining whether a pathogen can become epidemic or persist at endemic levels. Epidemic models that determine which interventions can successfully prevent an outbreak need to account for social structure and mixing patterns. Contact patterns vary across age and locations (e.g. home, work, and school), and including them as predictors in transmission dynamic models of pathogens that spread socially will improve the models’ realism. Data from population-based contact diaries in eight European countries from the POLYMOD study were projected to 144 other countries using a Bayesian hierarchical model that estimated the proclivity of age-and-location-specific contact patterns for the countries, using Markov chain Monte Carlo simulation. Household level data from the Demographic and Health Surveys for nine lower-income countries and socio-demographic factors from several on-line databases for 152 countries were used to quantify similarity of countries to estimate contact patterns in the home, work, school and other locations for countries for which no contact data are available, accounting for demographic structure, household structure where known, and a variety of metrics including workforce participation and school enrolment. Contacts are highly assortative with age across all countries considered, but pronounced regional differences in the age-specific contacts at home were noticeable, with more inter-generational contacts in Asian countries than in other settings. Moreover, there were variations in contact patterns by location, with work-place contacts being least assortative. These variations led to differences in the effect of social distancing measures in an age structured epidemic model. Contacts have an important role in transmission dynamic models that use contact rates to characterize the spread of contact-transmissible diseases. This study provides estimates of mixing patterns for societies for which contact data such as POLYMOD are not yet available.

          Author summary

          The risk of infectious disease transmission varies in different settings, for instance at home, at work or in the community, as a result of the different social structures and mixing patterns in those locations. These social structures vary across countries in different stages of development and with different demographics. Social contact patterns have been measured in a small number of countries, but in large swathes of the world, contact patterns are unmeasured, which makes it challenging to build mathematical or computer models of disease spread and control. In this work, we developed a modelling framework to combine social contact data from the past studies of contact patterns within eight countries in the EU with data from multiple data sources including the Demographic Household Surveys, World Bank and UN Statistics Division, to provide validated approximations to age-and-location-specific contact rates for 152 countries covering 95.9% of the world’s population.

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          Modelling disease outbreaks in realistic urban social networks.

          Most mathematical models for the spread of disease use differential equations based on uniform mixing assumptions or ad hoc models for the contact process. Here we explore the use of dynamic bipartite graphs to model the physical contact patterns that result from movements of individuals between specific locations. The graphs are generated by large-scale individual-based urban traffic simulations built on actual census, land-use and population-mobility data. We find that the contact network among people is a strongly connected small-world-like graph with a well-defined scale for the degree distribution. However, the locations graph is scale-free, which allows highly efficient outbreak detection by placing sensors in the hubs of the locations network. Within this large-scale simulation framework, we then analyse the relative merits of several proposed mitigation strategies for smallpox spread. Our results suggest that outbreaks can be contained by a strategy of targeted vaccination combined with early detection without resorting to mass vaccination of a population.
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            Estimating the impact of school closure on influenza transmission from Sentinel data.

            The threat posed by the highly pathogenic H5N1 influenza virus requires public health authorities to prepare for a human pandemic. Although pre-pandemic vaccines and antiviral drugs might significantly reduce illness rates, their stockpiling is too expensive to be practical for many countries. Consequently, alternative control strategies, based on non-pharmaceutical interventions, are a potentially attractive policy option. School closure is the measure most often considered. The high social and economic costs of closing schools for months make it an expensive and therefore controversial policy, and the current absence of quantitative data on the role of schools during influenza epidemics means there is little consensus on the probable effectiveness of school closure in reducing the impact of a pandemic. Here, from the joint analysis of surveillance data and holiday timing in France, we quantify the role of schools in influenza epidemics and predict the effect of school closure during a pandemic. We show that holidays lead to a 20-29% reduction in the rate at which influenza is transmitted to children, but that they have no detectable effect on the contact patterns of adults. Holidays prevent 16-18% of seasonal influenza cases (18-21% in children). By extrapolation, we find that prolonged school closure during a pandemic might reduce the cumulative number of cases by 13-17% (18-23% in children) and peak attack rates by up to 39-45% (47-52% in children). The impact of school closure would be reduced if it proved difficult to maintain low contact rates among children for a prolonged period.
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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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                Author and article information

                Contributors
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                12 September 2017
                September 2017
                : 13
                : 9
                : e1005697
                Affiliations
                [1 ] Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
                [2 ] Program in Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore, Singapore
                [3 ] Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
                [4 ] Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
                [5 ] Modelling and Economics Unit, Health Protection Agency Centre for Infections, London, United Kingdom
                Emory University, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-0528-798X
                http://orcid.org/0000-0002-6271-5832
                http://orcid.org/0000-0001-6658-8255
                Article
                PCOMPBIOL-D-16-02012
                10.1371/journal.pcbi.1005697
                5609774
                28898249
                7a8dee03-4216-47f4-8e67-bea02775ebe3
                © 2017 Prem 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
                : 9 December 2016
                : 25 July 2017
                Page count
                Figures: 6, Tables: 0, Pages: 21
                Funding
                Funded by: Centre for Infectious Disease Epidemiology and Research
                Award Recipient :
                Funded by: Centre for Infectious Disease Epidemiology and Research
                Award Recipient :
                Funded by: Communicable Disease Public Health Research Grant
                Award ID: CDPHRG/0009/2014
                Award Recipient :
                KP and ARC were supported by the Ministry of Defence, Singapore, via the Centre for Infectious Disease Epidemiology and Research. The research was also supported by a grant from the Ministry of Health, Singapore, to ARC (CDPHRG/0009/2014). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Epidemiology
                Infectious Disease Epidemiology
                Medicine and Health Sciences
                Infectious Diseases
                Infectious Disease Epidemiology
                Social Sciences
                Sociology
                Education
                Schools
                People and places
                Geographical locations
                Africa
                South Africa
                People and Places
                Population Groupings
                Age Groups
                People and Places
                Geographical Locations
                Asia
                People and places
                Geographical locations
                South America
                Bolivia
                People and places
                Geographical locations
                Europe
                European Union
                Germany
                Social Sciences
                Sociology
                Education
                Home Education
                Custom metadata
                vor-update-to-uncorrected-proof
                2017-09-22
                Projected contact matrices are available in the Supporting Information files. Data are from the POLYMOD study whose authors may be contacted at DOI: 10.1371/journal.pmed.0050074. DHS data can be requested from http://www.dhsprogram.com/ Other data are publicly available from World Bank http://data.worldbank.org/, UIS.Stat http://data.uis.unesco.org/, United Nations Statistics Division http://unstats.un.org/unsd/default.htm, International Labor Organization http://www.ilo.org/global/statistics-and-databases/lang--en/index.htm.

                Quantitative & Systems biology
                Quantitative & Systems biology

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