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

Published online by Cambridge University Press:  04 July 2016

CHRISTIAN L. VESTERGAARD
Affiliation:
Aix Marseille Univ, Univ Toulon, CNRS, CPT, Marseille, France emails: cvestergaard@gmail.com, genois.mathieu@gmail.com, alain.barrat@cpt.univ-mrs.fr
EUGENIO VALDANO
Affiliation:
Sorbonne Universités, UPMC Univ Paris 06, UMR-S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F-75013 Paris, France emails: eugenio.valdano@gmail.com, polettoc@gmail.com, vittoria.colizza@inserm.fr
MATHIEU GÉNOIS
Affiliation:
Aix Marseille Univ, Univ Toulon, CNRS, CPT, Marseille, France emails: cvestergaard@gmail.com, genois.mathieu@gmail.com, alain.barrat@cpt.univ-mrs.fr
CHIARA POLETTO
Affiliation:
Sorbonne Universités, UPMC Univ Paris 06, UMR-S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F-75013 Paris, France emails: eugenio.valdano@gmail.com, polettoc@gmail.com, vittoria.colizza@inserm.fr
VITTORIA COLIZZA
Affiliation:
Sorbonne Universités, UPMC Univ Paris 06, UMR-S 1136, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F-75013 Paris, France emails: eugenio.valdano@gmail.com, polettoc@gmail.com, vittoria.colizza@inserm.fr ISI Foundation, Torino 10126, Italy
ALAIN BARRAT
Affiliation:
Aix Marseille Univ, Univ Toulon, CNRS, CPT, Marseille, France emails: cvestergaard@gmail.com, genois.mathieu@gmail.com, alain.barrat@cpt.univ-mrs.fr ISI Foundation, Torino 10126, Italy

Abstract

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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Copyright
Copyright © Cambridge University Press 2016 

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References

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