Abstract
Social contact patterns among individuals encode the transmission route of infectious diseases and are a key ingredient in the realistic characterization and modeling of epidemics. Unfortunately, the gathering of high quality experimental data on contact patterns in human populations is a very difficult task even at the coarse level of mixing patterns among age groups. Here we propose an alternative route to the estimation of mixing patterns that relies on the construction of virtual populations parametrized with highly detailed census and demographic data. We present the modeling of the population of 26 European countries and the generation of the corresponding synthetic contact matrices among the population age groups. The method is validated by a detailed comparison with the matrices obtained in six European countries by the most extensive survey study on mixing patterns. The methodology presented here allows a large scale comparison of mixing patterns in Europe, highlighting general common features as well as country-specific differences. We find clear relations between epidemiologically relevant quantities (reproduction number and attack rate) and socio-demographic characteristics of the populations, such as the average age of the population and the duration of primary school cycle. This study provides a numerical approach for the generation of human mixing patterns that can be used to improve the accuracy of mathematical models in the absence of specific experimental data.
Highlights
The accurate characterization of the structure of social contacts in mathematical and computational models of infectious disease transmission is a key element in the assessment of the impact of epidemic outbreaks and in the evaluation of effective control measures
The transmissibility potential of a disease and the final epidemic size strongly depend on mixing patterns between individuals of the population, which in turn depend on socio-demographic parameters [1,2,3,4,5]
Empirical data collection on a large scale is extremely difficult and several models tackling both new emerging epidemics and endemic diseases have introduced a significant amount of information on contact patterns [3,5,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31], it is clear that the increasing use of data-driven models in the support of public health decisions is calling for novel approaches to the estimation of mixing patterns in human populations
Summary
The accurate characterization of the structure of social contacts in mathematical and computational models of infectious disease transmission is a key element in the assessment of the impact of epidemic outbreaks and in the evaluation of effective control measures. Unlike classical agent based approaches of epidemic transmission [3,5,15,17,19,32] and network models [33,34], which are aimed at characterizing the spatio-temporal spread of epidemics tagging each individual in the population with a set of social attributes, we use the same detailed information on social contacts to construct contact matrices by age in the different settings to be used in compartmental models This approach integrates population details, providing an effective description of the population structure to be used in computational models relying on compartmental schemes both at the continuous and individual based scale. Such a strategy might be very convenient to reduce the computational time demand in the analysis of large scale geographical models [21,35,36,37,38,39]
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