Projecting social contact matrices to different demographic structures

Abstract

The modeling of large-scale communicable epidemics has greatly benefited in the last years from the increasing availability of highly detailed data. Particullarly, in order to achieve quantitative descriptions of the evolution of epidemics, contact networks and mixing patterns are key. These heterogeneous patterns depend on several factors such as location, socioeconomic conditions, time, and age. This last factor has been shown to encapsulate a large fraction of the observed inter-individual variation in contact patterns, an observation validated by different measurements of age-dependent contact matrices. Recently, several works have studied how to project those matrices to areas where empirical data are not available. However, the dependence of contact matrices on demographic structures and their time evolution has been largely neglected. In this work, we tackle the problem of how to transform an empirical contact matrix that has been obtained for a given demographic structure into a different contact matrix that is compatible with a different demography. The methodology discussed here allows to extrapolate a contact structure measured in a particular area to any other whose demographic structure is known, as well as to obtain the time evolution of contact matrices as a function of the demographic dynamics of the populations they refer to. To quantify the effect of considering time-dynamics of contact patterns on disease modeling, we implemented a Susceptible-Exposed-Infected-Recovered (SEIR) model on 16 different countries and regions and evaluated the impact of neglecting the temporal evolution of mixing patterns. Our results show that simulated disease incidence rates, both at the aggregated and age-specific levels, are significantly dependent on contact structures variation driven by demographic evolution. The present work opens the path to eliminate technical biases from model-based impact evaluations of future epidemic threats and warns against the use of contact matrices to model diseases without correcting for demographic evolution or geographic variations.

Data-driven contact structures: From homogeneous mixing to multilayer networks

“…In the M model, the size of each age-group is computed using the same procedure. Besides, the age-mixing matrix was corrected so that reciprocity is fulfilled, and the average connectivity is exactly 19.40 [50]. In the C model, we randomly extract the degree of each node from a right-censored negative binomial distribution adjusted to the survey data from POLYMOD [14].…”

“…Since then, a number of studies covering different countries have appeared, although data on Africa and Asia are still scarce [46]. Various methods have been developed to infer the contact patterns in the absence of direct data [47][48][49], and to project them into the future [50]. And yet, most studies that use this data disregard the whole distribution of contacts and use only the average number of contacts between groups, completely neglecting the individual heterogeneity (with few exceptions [51]).…”

Estimating COVID-19-Related Infections, Deaths, and Hospitalizations in Iran Under Different Physical Distancing and Isolation Scenarios

“…We first incorporated the value of 14 in model for Tehran and 13 in the national model in the early weeks of the epidemic. [11][12][13][14][15] After the announcement of the epidemic by health officials, multiple public health measures were implemented as a response to the epidemic to reduce contact and transmission rates in the public. Approaching the assumed end of the epidemic, we considered the value of 5 for the contact rate parameter with some fluctuations due to Nowruz holidays which overlapped with this period.…”

Demographic Situation In The Volga Economic Region

“…The study of the regional demographic situation and the identification of its differentiation aspects should be carried out on the comparative analysis basis for a certain geographically compact set of regions (Arregui, Aleta, Sanz, & Moreno, 2018). We will accept the Volga economic district as the object of the study, which has eight subjects of the Russian Federation.…”