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Fournet, Julie and Barrat, Alain 2017. Estimating the epidemic risk using non-uniformly sampled contact data. Scientific Reports, Vol. 7, Issue. 1,
Zino, Lorenzo Rizzo, Alessandro and Porfiri, Maurizio 2016. Continuous-Time Discrete-Distribution Theory for Activity-Driven Networks. Physical Review Letters, Vol. 117, Issue. 22,
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The ability to directly record human face-to-face interactions increasingly enables the development of detailed data-driven models for the spread of directly transmitted infectious diseases at the scale of individuals. Complete coverage of the contacts occurring in a population is however generally unattainable, due for instance to limited participation rates or experimental constraints in spatial coverage. Here, we study the impact of spatially constrained sampling on our ability to estimate the epidemic risk in a population using such detailed data-driven models. The epidemic risk is quantified by the epidemic threshold of the SIRS model for the propagation of communicable diseases, i.e. the critical value of disease transmissibility above which the disease turns endemic. We verify for both synthetic and empirical data of human interactions that the use of incomplete data sets due to spatial sampling leads to the underestimation of the epidemic risk. The bias is however smaller than the one obtained by uniformly sampling the same fraction of contacts: it depends non-linearly on the fraction of contacts that are recorded, and becomes negligible if this fraction is large enough. Moreover, it depends on the interplay between the timescales of population and spreading dynamics.
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