To this end, we combined data from POLYMOD, from the large scale Demographic Household Surveys (DHS), from the UN population division and from various international indicators, to project household structures and school and labor force participation rates for most countries of the world, and thereby to provide baseline projections of age-specific contact patterns in settings where contact surveys have yet to be conducted, until empirical estimates become available. Contact matrices for Bolivia (DHS country; in panels b,e,h,k) and South Africa (ROW country; in panels c,f,i,l) were projected and the age-specific mean contact rates for Germany (part of the POLYMOD; in panels a,d,g,j) were estimated from the German contact data. contains the average numbers of reported contacts by participant age group. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore, DHS data can be requested from Other data are publicly available from World Bank, UIS.Stat, United Nations Statistics Division, International Labor Organization Such a matrix However, these precise age-dependent patterns differed by location and across the countries we studied. Modelled projections of contact patterns in different locations—home, work, school, other and all—for Bolivia, Germany and South Africa, are plotted in Fig 5. No, Is the Subject Area "Germany" applicable to this article? [58] estimated contact matrices for 26 European countries. Source Fig 1 gives an overview of the data sources discussed in the following section, and the major steps of the modelling framework described in the sections that follow that. This study provides estimates of mixing patterns for societies for which contact data such as POLYMOD are not yet available. No, Is the Subject Area "South Africa" applicable to this article? In Germany, for which it was explicitly measured, the age-specific contact patterns (panels d–f) at the workplace show wide clusters of contacts among working ages (20–60), indicating relatively homogenous mixing in this setting. Yes Yes The estimated mean number of contacts (red dots) made by an individual and the 95% confidence interval (orange lines) are shown. A central diagonal is present for all household sizes, indicating assortativity of mixing with age, with secondary diagonals in households with at least 2 members. However, school closure and social distancing of younger individuals is expected to be effective in preventing an outbreak entirely in younger populations like Bolivia and South Africa, Workplace distancing has a greater impact on older populations like Germany (preventing an outbreak entirely for R0 = 1.2 and reducing its size by ~50–70% for R0 = 1.5) than younger populations like Bolivia and South Africa (~30–70% for R0 = 1.2 and ~10–50% for R0 = 1.5). Writing – original draft, For all three countries, both the household age matrices (panels d–f) and age-specific contact patterns at home (panels g–i) have similar features: (i) a prominent central diagonal corresponding to interactions with siblings (for younger individuals) and partners (for adults) and (ii) two parallel secondary ridges about one generation distant from the main diagonal, which start around age 25 and reflect parent-child contacts. To demonstrate the application of the projected contact matrices, we performed age-structured Susceptible-Infected-Removed (SIR) modelling [39] using the derived age-specific contact matrices for countries of different levels of development. The ensuing subsections detail the methodology adopted to construct age-structured populations at home, work, and school (B) is combined with the population age structure and the POLYMOD aggregated estimates to get the global projections (C). The Fumanelli estimates [58] in particular did not reproduce the narrowness of the leading diagonal in the contact matrix observed in the POLYMOD study, in contrast to our approach (more in the S1 Text). Polymod matrix), suggesting that for highly transmissible infections only the number of contacts matters. The default setting produces the six age groups of Meyer and Held (2017). Individuals were connected in a dynamically evolving age-dependent contact network based on the POLYMOD study. We determined the number of teachers in country c using data on pupil-teacher ratios (class size ) from UIS and number of students by the equation (contacts), labelled with the corresponding age groups. Similarly to the approach used for workplaces, we projected the age-specific proportion of the population in school, including teachers, before modelling the contact patterns in the school. A summary of the methodology is represented by the model framework: (A) POLYMOD model, (B) construction age-structured populations at home, work, and school in the 152 countries, and (C) projection of global estimates. Some of these synthetic contact matrices were created for only one country (Hong Kong [56] and Italy [57]), while Ref. (2008) Social Contacts and aggregated over the corresponding column (contact) age groups. PolyMod® Technologies Inc. AS9100D & ISO 9001:2015 Registered QMS. either "all" contacts, i.e., count both physical and pure Author(s) The projected contact matrices for the POLYMOD countries when treated as an ROW country were similar to the POLYMOD contact data, thus validating the ROW procedure. Those limitations notwithstanding, the projected contact matrices outlined in this paper provide a basis for model-based analyses to inform public health policy making around the world, until comprehensive studies can be carried out that cover a greater fraction of the world’s population. Box 10180 Fort Wayne, IN 46851-0180 USA Phone: +01 (260) 436-1322 Fax: +01 (260) 432-6051 Email: data available at i.e. For two pandemic influenza scenarios (R0 = 1.2 and 1.5), the age-specific final epidemic size and the percent reduction in infection were calculated for three scenarios: No intervention (total contacts calculated as a sum of contacts made at home, work, school and other), School closure and social distancing of younger individuals (zero contribution from school contacts and reduction in contacts at other locations with individuals below 20 years and a small increase in contacts made at home) and Workplace distancing (reduction of work contacts by a half). To obtain projected regional norms for contact patterns at home we computed the mean contact rates that were projected or inferred earlier within the region weighted by each country’s population size. The contact pattern at all locations (panels m–o) is the sum across the four locations (home, work, school and others). (ii) The population age compositions for all countries of the world were obtained from the United Nations Statistics Division. This data formed part of POLYMOD, a European Commission project funded within the Sixth Framework Programme E-mail for: Customer Serviceor general inquiries: This e-mail address is being protected from spambots. For the Asian countries in the DHS samples, noticeable tertiary diagonals, reflecting three generation households, were present, which highlight the limitations of using data from Europe to represent non-European societies without adjustments similar to those performed in this analysis. This could be related to biases in contact reporting (Smieszek and others, 2014) with more unreported (short) contacts along the diagonal. Moreover, quantifying social contact mixing patterns and their variation Examples. Writing – original draft, These secondary diagonals become more prominent with increasing household sizes. If which="reciprocal" (corresponding to contactmatrix_wallinga The countries (n = 152, 95.9% of the world’s population) are categorized into (i) POLYMOD, (ii) Demographic and Health Survey (DHS), and (iii) Rest of the World (ROW) countries, which are illustrated on the world map. The two have a generally close correspondence, within the constraints of the … The findings from the POLYMOD and limited number of other countries cannot therefore be directly applied to models of socially-transmitted infections, such as influenza, in other settings [32]., Editor: Betz Halloran, Emory University, UNITED STATES, Received: December 9, 2016; Accepted: July 25, 2017; Published: September 12, 2017. The original age groups (5-year intervals) can be joined together by the grouping argument, which first sums over contact groups (columns) and then averages over the corresponding … The mean was for home contacts, for work contacts, for school contacts and for contacts at all other locations. Because should be proportional to , we improved the raw projections by generating 10 000 bootstrap samples of the pairwise distances between countries (between indicators) as a ‘prior’ distribution, and selected the combination that maximized the correlation between and . The population pyramids of Bolivia (a DHS country, panel b) and South Africa (a ROW country, panels c) in Fig 3 have the triangular shape common to countries still undergoing the demographic transition, while that of Germany (in POLYMOD, panel a), with its narrow base, indicative of sub-replacement fertility, is similar to other aging populations. To extend the contact rate model to other countries of the world, we synthesized the POLYMOD data with four other data sources that either inform contact patterns in households, workplaces and schools, or provide a measure of the similarity of countries. For larger values of the MSD (infections with lower transmissibility), the importance of the number of contacts decreases and that of the exposure duration increases., This function requires a number of parameters and we will explain all these parameters below. Visualization, greater tendency of having a household member of that age, higher proclivity of making the age-specific contact. A landmark dataset of self-reported contacts was the POLYMOD study [22], which collected ... context (home, work, school, other), resulting in 8 matrices each for the raw contact matrix shown in Fig S1 and population contact matrix in Fig 3. High assortativity of contacts is observed in schools but, at least in the POLYMOD countries, is less apparent in working-age individuals in the workplace. Having projected the household age matrix for ROW countries, for both DHS and ROW countries, the age-specific contact at home for country c is then set to: Heterogeneities in contact networks have a major effect in determining whether a pathogen can become epidemic or persist at endemic levels. Program in Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore, Singapore, While epidemic models that do not account for contact structure suffice for some research questions, such as prediction [15] or determining minimum vaccine coverage if there are no pockets of high transmission intensity or low coverage [16–19], assessing the effectiveness of interventions that specifically target social networks, such as school closure [20–22], requires models that explicitly account for such social structure. Arguments C. a square numeric contact matrix such as contactmatrix_POLYMOD. Format As in the original POLYMOD study, we only quantified age-specific dyadic contacts, as eliciting higher order contacts in a survey is challenging both cognitively and practically. Research in social networks has shown that transmissibility, and hence the effectiveness of many interventions, is determined by the intensity of human-to-human interactions [12]. Similar studies to measure the assortativity of contacts have been conducted in a few other locations: Viet Nam [25], Taiwan [26], southern China [27], Peru [28] South Africa [29], Kenya [30], Russia [31] and Thailand [32]. The parameters q and q are here interpreted as “level” and “shape” param- 1 2 eters, respectively. Darker color intensities indicate higher proclivity of making the age-specific contact. Intense mixing, indicated by the pronounced central diagonal, is present in the age-specific school contact pattern (panels g–i), suggesting the importance of this milieu for transmission potential within this age group. (6), The school population distribution of ages (Sc) with elements signifying the probability of encounters between two ages within school was obtained using The research was also supported by a grant from the Ministry of Health, Singapore, to ARC (CDPHRG/0009/2014). The scree plot of the eigenvalue decomposition of these variables suggested that beyond 4 or 5 dimensions, additional variables did not add much new information, but alternative measures of distance would have resulted in some differences in the projected matrices. For small values of the MSD, we obtain the standard contact matrix itself (i.e. The distribution of age of infected cases under two pandemic scenarios is presented in Fig 6 for Germany, Bolivia and South Africa. The present study aimed to conduct a contact survey in Japan, offering estimates of contact by age and location and validating a social contact matrix using a seroepidemiological dataset of influenza. We had substantially less data on potential school and work contacts, because direct samples from these environments were not available, even in POLYMOD, and so the approach extrapolated from indirect measures (workforce and school participation rates, teacher to student ratios). Yes in Table S5 of Mossong et al. For individuals in the age group 55–60, the mean number of contacts made with younger individuals in South Asia is significantly higher than those in other regions. The infection probability per individual contact per day was calibrated to be 0.03 for the German model by com-paring the simulation output with observed data from the 2006/07 German influenza season [4]). "corrected" (from contactmatrix_POLYMOD or 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. Contact matrix According to the ages of each respondent–contactor pair, reported contacts were grouped into 15 discrete age groups (0–4, 5–9, …,65–69, and 70years or older). The inference was implemented using Just Another Gibbs Sampler (JAGS) [36] within the R statistical environment (R Core Team, 2013) using the rjags package [37] with 100 000 iterations for each of the eight POLYMOD countries independently and collectively. Description Diaries of young children were completed by a parent or guardian. age-specific Susceptible-Infected-Removed modelling) that demonstrates the potential utility of these projections. 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. Glass and Glass [24] found similar assortativity among younger age groups, and proposed that this assortativity made those in younger age groups the transmission backbone of respiratory epidemics. Information on enrolment rates by education level and age ranges of education were obtained from UIS. PLoS Comput Biol 13(9): The main function implementing the epidemiological model is the infectionODEs function. the returned social contact matrix fulfils reciprocity of contacts with The labor force participation rate of age a in country c, , was obtained from the International Labor Organization and used to compute a joint distribution of the working population, (Wc), a square matrix with elements describing the probability of encounters between two ages in the workforce, . The Polymod contact matrix for Italy was incorporated to simulate the heterogeneity of contacts by age [3]. To illustrate some of the results, Germany, Bolivia, and South Africa are arbitrarily selected as country representatives of the POLYMOD, DHS and ROW, respectively. Formal analysis, We assumed the number of contacts in each location was Poisson, with over-dispersal accounted for using an individual-level random effect term that was assumed to govern contacts in all four locations, but in principle additional heterogeneities might be present leading to mischaracterization of correlations, dispersion [60], and the number of zeros. Meyer S and Held L (2017): Incorporating social contact data in a logical indicating whether to normalize the matrix This approach did, however, mean excluding several small countries with missing information, covering 4.1% of the world’s population. Events over the last decade have highlighted the threat posed internationally by contact-transmissible infectious diseases such as hand, foot and mouth disease [1,2], MERS-CoV [3], Ebola [4], influenza [5], and Tuberculosis [6], putting pressure on governments and public health institutes [6] to ensure countries are pandemic-prepared. Leave-one-out validation was performed to reconstruct the HAM of one POLYMOD or DHS country at a time. The population pyramids by age and gender (panels a–c), household age matrices (panels d–f) and age-specific contact patterns (panels g–i) are presented for Germany (first column, as a representative of the POLYMOD countries), Bolivia (second column, as a representative of DHS) and South Africa (third column, as a representative of ROW). 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. Fax: +1 (260) 432-6051. home, work, and school), and including them as predictors in transmission dynamic models of pathogens that spread socially will improve the models’ realism. respect to the age distribution of Berlin, pop2011, specification of how to aggregate groups (a named list or The POLYMOD mixing matrices were derived from population surveys conducted in 2006 and 2012 ; therefore, the data may not be appropriate to reflect the contact patterns up to 50 years earlier, even after adjusting the mixing matrix with annual population changes. However, the age and social structure of countries with different levels of socioeconomic development, family structure and at different stages of demographic transition differ substantially and contact patterns concomitantly vary across countries. for Germany from the POLYMOD survey (Mossong et al., 2008). Data Availability: Projected contact matrices are available in the Supporting Information files. An internet-based questionnaire survey was conducted, … To derive typical contact patterns at home for countries not present in POLYMOD or DHS (rest of world, or ROW), and hence without household structure data, we projected the household age matrix (HAM) for country c, , equal to the mean number of household members of age α of an individual aged a. or contactmatrix_wallinga_physical as used by Meyer and Held, 2017), The second major modelling step was to project from POLYMOD to non-POLYMOD countries the relationship between (i) household structure or workforce/school participation and (ii) contact rates within those locations. It is not clear how this approach could be improved for these location types, or for ‘other’ locations, however, without collecting substantial amounts of new data. If NULL, the original 5-year intervals are returned. Similar to the home and school contact patterns, a strong central diagonal band and at times weak secondary diagonals can be observed in the projected contacts made at other (non-home, non-work, non-school) locations. The variables were arbitrarily selected to span measures of health and social structure (fertility, mortality, growth, and health expenditure), as well as development (income, internet penetration and urbanization). A decade after POLYMOD, age-structured contact matrices based on random population-based samples have been published for only a limited spectrum of countries: eight European Union countries [23], a handful in Asia [25–27,31,32], one in Latin America [28] and two in Sub-Saharan Africa [29,30]. Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore, Roles In order to validate the data driven modeling approach at the origin of the synthetic contact matrices we compared our matrices with those obtained by the Polymod project . in 2008 [23] measured the social structure of ~100 000 contacts across eight European countries using paper diaries as part of the POLYMOD project. : Funding acquisition, The empirical household age-structures for the POLYMOD and DHS countries were reconstructed with high fidelity (median correlation between inferred and empirical 0.93, inter-quartile range 0.91–0.95; plotted in the S1 Text). related to the age group 70+ (see the Examples) and is the default. This study presents data-driven contact matrices for 152 countries of the world for the first time. To assess the robustness of our results to this critical assumption, we calculated a contact matrix in Massachusetts using the method described in ( 40 ). Synthetic contact matrices have been generated by individual-based model simulations [56] or derived from socio-demographic variables [57,58] and validated on serological data of H1N1 Influenza [56,58], varicella and parvo-virus [57] using age-structured SIR models. A near linear relationship is observed between the household size and the number of contacts, suggesting a frequency dependent relationship, rather than a density dependent one [41]. [40]. Usage The regional mean age-specific contact patterns at home (inferred) of individuals aged 5–10 (first column), 25–30 (second column) and 55–60 (third column) years were represented as bars. grouping argument, which first sums over contact groups (columns) and The original POLYMOD matrices use 15 categories which usually group individuals in 5-year age classes, and have an asymmetric structure, … specific POLYMOD matrix [1] as summarized in Table 1. With the population in school known we can now deduce the possible age-specific contact patterns in school by the following expression: grouping. These social structures vary across countries in different stages of development and with different demographics. Within schools, the assortativity of mixing patterns is more pronounced in younger individuals (below the age of 25), while those of working-age (teachers and support staff aged 30–60) have more moderate contact rates between themselves and younger individuals, the latter characterizing student-teacher interactions. Contact matrices for India and Bangladesh have additional tertiary bands (S1 Text), suggesting a greater preponderance of three-generation households. here. 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. Software, they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. However, it remains to be demonstrated empirically that the differences in the patterns of transmission of an infection between two countries can be explained by differences in their contact patterns, although our simulation study in three countries suggested that the effect of interventions can vary substantially based solely on changes in contacts driven by age structure. Age-Structured Spatio-Temporal Models for Infectious Disease Counts, ## contact matrix reported in Mossong et al (2008, Table S5), ## this simply returns the dataset 'contactmatrix_mossong', ## with corrected numbers for the 70+ age group (the default), ## this simply returns the dataset 'contactmatrix_POLYMOD', ## compare entries of last row and last column, ## contact matrix estimated to be reciprocal on the population level, ## this simply returns the dataset 'contactmatrix_wallinga', ## visually compare raw to reciprocal contact matrix, ## select physical contacts and aggregate into 5 age groups, ## the default 6 age groups, normalized to a transition matrix, ## reciprocity also holds for this grouping, hhh4contacts: Age-Structured Spatio-Temporal Models for Infectious Disease Counts,

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