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The ability to directly record human face-to-face interactions increasinglyenables the development of detailed data-driven models for the spread ofdirectly transmitted infectious diseases at the scale of individuals. Completecoverage of the contacts occurring in a population is however generallyunattainable, due for instance to limited participation rates or experimentalconstraints in spatial coverage. Here, we study the impact of spatiallyconstrained sampling on our ability to estimate the epidemic risk in apopulation using such detailed data-driven models. The epidemic risk isquantified by the epidemic threshold of thesusceptible-infectious-recovered-susceptible model for the propagation ofcommunicable diseases, i.e. the critical value of disease transmissibilityabove which the disease turns endemic. We verify for both synthetic andempirical data of human interactions that the use of incomplete data sets dueto spatial sampling leads to the underestimation of the epidemic risk. The biasis however smaller than the one obtained by uniformly sampling the samefraction of contacts: it depends nonlinearly on the fraction of contacts thatare recorded and becomes negligible if this fraction is large enough. Moreover,it depends on the interplay between the timescales of population and spreadingdynamics.

Impact of spatially constrained sampling of temporal contact networks on the evaluation of the epidemic risk