polymod contact matrix

(8), We determined age-specific contact pattern at other (non-home, school or work) locations by multiplying the ratio of population age structure of the country and the POLYMOD countries to the estimates of obtained from the POLYMOD analysis, corresponding age distribution as weights. Mixing matrices can be generated from surveys that record the number and type of contacts between people, such as the respondent-completed diaries used in the landmark POLYMOD contact pattern study, which measured social contact patterns in eight European countries (20). 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, https://www.researchgate.net/publication/232701632_POLYMOD_contact_survey_for_researchers. 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. Writing – original draft, Convergence of the Markov chain Monte Carlo samplers using the Heidelberger-Welch diagnostic which was passed for 89% of parameters (median effective sample size 52 000, inter-quartile range 20 000–78 000). (6), The school population distribution of ages (Sc) with elements signifying the probability of encounters between two ages within school was obtained using 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]. Together, these suggest that the contacts are dominated by two-generation familial structures for these three countries, although other countries such as India display evidence of three-generational structures. Graphs were created in the grid package [38] in R. Two forms of validation were conducted. (ii) The population age compositions for all countries of the world were obtained from the United Nations Statistics Division. The main function implementing the epidemiological model is the infectionODEs function. This has the added benefit of providing a means to model location-specific interventions. In order to be able to compare different countries, this value was used for all countries. This means that the number and age-profile of contacts outside the home are inherently less well projected by our approach than those within the home. The objective of this paper is to provide projected age-specific contact rates for countries in different stages of development and with different demographic structures to those studied in POLYMOD, which provide validated approximations to social contact patterns when directly measured data are not available. 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. , the number of contacts made by individual i at a particular location L (home, work, school or other) with someone in age group α, is assumed to be Poisson with mean , with a random effect σi for individual i. The function contactmatrix retrieves various social contact matrices 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. grouping argument, which first sums over contact groups (columns) and PolyMod® Technologies. Investigation, These projections were compared to the empirical POLYMOD data (S1 Text), We also externally validated the projected contact matrices by comparing the projected matrices to recently conducted contact surveys in five low and middle income countries in three continents (Kenya, Peru, Russia, South Africa, and Viet Nam [25,28–31]), as described in the S1 Text. Individuals were connected in a dynamically evolving age-dependent contact network based on the POLYMOD study. It contains the mean number of contacts that each member of an age group (row) has reported with members of the same or another age group (column). 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. The default setting produces the six age groups of Meyer and Held (2017). home, work, and school), and including them as predictors in transmission dynamic models of pathogens that spread socially will improve the models’ realism. 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. The POLYMOD study design has been fully described elsewhere [23]. 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]. Adapting this finding to Bolivia and South Africa, accounting for the age structure of their labor forces, led to similar homogeneity in workforce contacts there. This approach implicitly assumes that given a potential contact in a given location (for instance, the existence of a cohabitant), the chance of an actual interaction is the same in POLYMOD and non-POLYMOD countries. There were several countries, such as Sierra Leone and Burkina Faso, where we infer a deviation from that pattern as a result of skewed population structure (S1 Text). Investigation, For more information about PLOS Subject Areas, click The two have a generally close correspondence, within the constraints of the small sample sizes when stratified by age in the Kenyan and Vietnamese studies, which lead to relatively large standard errors and more of an apparent discrepancy. These social structures vary across countries in different stages of development and with different demographics. Contact matrix and demography strongly influence the age-dependent incidence of influenza and the success of vaccination. Third, in the absence of empirical contact data in the United States, the results presented in the main text were obtained using the POLYMOD contact matrix in Great Britain. Routine public health surveillance of notifiable infectious diseases gives rise to weekly counts of reported cases—possibly stratified by region and/or age group. Writing – original draft, e1005697. The number of cohabitants of i of age α, νi,α, represents household age structure, while wi and si indicate if i went to work or school on the day of the survey. Meyer S and Held L (2017): Incorporating social contact data in [58] estimated contact matrices for 26 European countries. There are several assumptions underlying our study. The reciprocal contact matrices contactmatrix_wallinga and specification of how to aggregate groups (a named list or Yes Data Availability: Projected contact matrices are available in the Supporting Information files. Value This involved projecting that country’s household age structure as if it were unknown and comparing against the actual structure. Arguments The age-specific contact matrices observed in five contact surveys in non-POLYMOD countries were compared against our projected matrices (S1 Text). Such a matrix contains the average numbers of reported contacts by participant age group. 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. Our extension of POLYMOD to most countries of the world involved two main modelling steps with inherent assumptions, elaborated below. Modelling and Economics Unit, Health Protection Agency Centre for Infections, London, United Kingdom. Despite the lack of social contact surveys, synthetic contact matrices have only developed for higher-incomed countries [56–59], with large proportions lower-and-middle-income countries unrepresented. Panel a shows the number of contacts made by individuals at home (grey dots) in the POLYMOD study stratified by household sizes. prop.table(colSums(pop2011)). E-mail for: Customer Serviceor general inquiries: This e-mail address is being protected from spambots. the returned social contact matrix fulfils reciprocity of contacts with 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. The study collected information of 97,904 contacts. The population pyramids, panels a–c, and household age matrices (for only POLYMOD and DHS), panels d–e, are observed data. The original POLYMOD study found highly assortative mixing in ‘other’ locations, and while the study in Russia [31] found a similar pattern, it is not clear whether this would be observed in other, non-European or Eurasian settings. i.e. This study provides estimates of mixing patterns for societies for which contact data such as POLYMOD are not yet available. As in the original analysis of these data [23], we found that household, workplace and school structures across the world are consistent with age-specific contacts made by individuals, in that they are highly assortative, with much more frequent interactions with others of a similar age group. In the analysis, 52 participants (0.7%) and 148 contacts (0.2%) were excluded for data quality reasons as detailed in the S1 Text. 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. potentially averaged over multiple row (participant) age groups variants gives the age distribution of Berlin, i.e.,

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