Virtually every government with ‘universal’ health care restricts access in one way or another to control costs. B: Proportion of the overall connectivity that comes from individual with less than 20 years (Y) vs the assortativity coefficient (r) for the 16 settings. COVID-19 Regional Risk Matrix provides data and public health recommendations for local health departments and schools to consider based on the level of COVID-19 transmission in their region. In Poland we see that M0 and M1 tend to underestimate the incidence specially among the elder age-groups. Applying different methods to deal with contact patterns leads to important differences not only in the global incidence for a SEIR model, but also on age-specific incidences. Contact; Contact. Citation: Arregui S, Aleta A, Sanz J, Moreno Y (2018) Projecting social contact matrices to different demographic structures. Particullarly, in order to achieve quantitative descriptions of the evolution of epidemics, contact networks and mixing patterns are key. In the examples shown here, only two years before/after the survey time, the fraction of non-reciprocal contacts already reaches 5%. Having such an important impact for the spreading of a disease, the insights provided by this work should be taken into consideration by modelers and also by public health decision-makers. Notice that all methods trivially coincide in the year in which the data was obtained (i.e. These trends are decreasing in Europe and increasing in Africa. Thus, for any meaningful epidemic forecast based on a model containing age-mixing contact matrices, we would need to adapt them taking into account the evolution of the demographic structures. This imposes the following closure relation for the contact matrix: Yes Here are just a … See the Supplementary Information for more details. Finally, we note that there are some limitations that could affect quantitatively the results shown in this work. As represented in Fig 1D and 1E, for M1 we also have an underrepresentation of contacts between adults and an overestimation between young individuals, yielding to similar results to M0. We plan to explore these issues in the future. As contact matrices play a key role in disease forecast, it is essential to assure that the matrices implemented are adapted to the demographic structure of the population considered in order to avoid biased estimations. Furthermore, it remains unknown to what extent the variations between contact matrices coming from different geographic settings are due to differences in the demographic structures, divergent cultural traits and/or methodological differences between studies. There are some methodological differences between these studies, thus some pre-processing to homogenize the matrices is required. That is a sign that the virus is probably more widespread in New York than in California. In brief, when we use directly a contact pattern in a demographic structure that is younger than when it was measured, it will lead to an overestimation of the contact rate of (and the force of infection corresponding to) the youngest age-groups. which allows to rewrite Eq 3 as a function of the original matrix Mi,j: The "elements" or scores in the cells of the matrix record information about the ties between each To reach out to the Matrix Hotline, please call: 1-888-777-6396. https://doi.org/10.1371/journal.pcbi.1006638.g002. when the survey was done). A paradigmatic example of this kind of situation is the modeling of seasonal influenza, that typically involves calibration of each year strains’ infectiousness at the early onset of the season outbreak. Please share any questions, concerns, and feedback you have for us. Taking that potential issue into consideration, we have proposed an alternative approach that, in addition of correcting for the densities of contactees, preserves the mean connectivity of the overall system across time. Elderly Italian people, while most of them live by themselves, are not isolated, and their life is characterised by a much more intense interaction with their children and younger population compared to other countries. Mapping and assessing needs (social issues) 3. Social Science Matrix. It details things like project owners, deadlines, project status, objectives, and so on. We also compare empirical contact matrices of 16 countries and regions in different areas worldwide filtering the influence of the demographic structure. (5). However, a general discussion on the side implications of these corrections and their range of applicability is still missing. In Zimbabwe M0 tends to overestimate the incidence among young individuals, while with M1 we encounter both effects: and overestimation among the youngest and a underrepresentation among the eldest. Methodology, We also evaluate the impact of an eventual future outbreak by simulating a SEIR scenario in the countries and regions analyzed. But the problem can appear even if we remain in the same geographical setting, as a contact matrix measured at a specific time τ, could not be valid for an arbitrary time t if the demographic structure of that population has changed. We can obtain these matrices Γi,j, that are country-specific, from survey data using Eq 3: Similarly, in other epidemiological studies, when implementing heterogenous contact patterns, modelers apply different corrections to solve the problem of non-reciprocity [7, 8, 11, 28, 29]. To this end, we implement a Short cycle SEIR model (details can be found in the Supplementary Information) to study a situation where a short-cycle, influenza-like pathogen appears in a given location in subsequent times. A cardinal feature of M2 is that it does not preserve the mean connectivity of the entire network of contacts. Summing up, using each of the different methods here described can result into significantly different projected contact patterns and modelers should be aware of the implications that this has on disease modelling. 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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. While M3 leads to an outbreak size that is essentially invariant in time -due to stochasticity-, the outcome predicted from M2 is highly variable.  published a seminal work with the measurements of age-dependent contact rates in eight European countries (Belgium, Finland, Germany, Great Britain, Italy, Luxembourg, Netherlands and Poland) via contact diaries. Name * Email * Department/Affiliation . Given the utmost importance of contact heterogeneities, the study of age-dependent social mixing has become a priority in the field. Consensus, where I went to college, was that status and prestige were unquestionably good things. regarding the demographic projections used, they were retrieved from the UN population division database . In Fig 2A and 2B we represent the contact patterns obtained with M2 and M3 for Poland and Zimbabwe, respectively, in different years. The entries of Γi,j are bigger than 1 when the interactions between age-groups i and j surpasses what it is expected from the case of homogeneous mixing, and smaller than 1 in the opposite case. Leave this field blank . On the other hand, regarding the comparison between the two methods based on the density correction for available contactees -M2 and M3-, we have seen how the introduction of a normalization term in M3 aimed at preserving the overall connectivity is specially relevant in the cases where epidemiological parameters cannot be calibrated at the early stages of the epidemic phenomena to be modelled. If any query or just wanna say hello feel free to contact us! The comparison of the age distributions from methods M2 and M3 (Fig 5D) shows that the differences between both methods, already discussed at the aggregated level, also occur in the same direction within all age groups. The Internationally Leading Center for Digital Humanities & Social Sciences. In particular, scenario 1 is instrumental to distinguish the outcomes from models M0 and M1 from either M2 or M3. In the case of European countries (Poland in panel A as an example), demographic structures have suffered from an ageing process during the last decades, which is predicted to continue in the future. It is also worth pointing out that, although in this work we have focused on age-structured systems (which has had its relevance in recent history of epidemiology), the problem studied here can be extrapolated to other models that might categorize their individuals based on other different traits that determine their social behavior. C: Incidence by age group for Poland and Zimbabwe in 2010, 2030 and 2050 using M2. Due to the high cost of gathering empirical data on social contacts, Fumanelli et al. In turn, scenario 2 simulates a situation where the election between M2 or M3 becomes of central relevance, since the basic reproductive number of outbreaks will now depend on the contacts produced by each method. They swab nasal passages for testing with RT-polymerase chain reaction (rRT-PCR), then they send the patient home to wait for test results in isolation. R package for deriving social mixing matrices from survey data.
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