title: Compensating for population sampling in simulations of epidemic spread on temporal contact networks.
abstract: Data describing human interactions often suffer from incomplete sampling of the underlying population. As a consequence, the study of contagion processes using data-driven models can lead to a severe underestimation of the epidemic risk. Here we present a systematic method to alleviate this issue and obtain a better estimation of the risk in the context of epidemic models informed by high-resolution time-resolved contact data. We consider several such data sets collected in various contexts and perform controlled resampling experiments. We show how the statistical information contained in the resampled data can be used to build a series of surrogate versions of the unknown contacts. We simulate epidemic processes on the resulting reconstructed data sets and show that it is possible to obtain good estimates of the outcome of simulations performed using the complete data set. We discuss limitations and potential improvements of our method.
authors.affiliation: Aix Marseille Université, Université de Toulon, CNRS, CPT, UMR 7332, 13288 Marseille, France.
authors.forename: Mathieu
authors.initials: M
authors.lastname: Génois
authors.affiliation: Aix Marseille Université, Université de Toulon, CNRS, CPT, UMR 7332, 13288 Marseille, France.
authors.forename: Christian L
authors.identifier: http://orcid.org/0000-0001-5329-475X
authors.initials: CL
authors.lastname: Vestergaard
authors.affiliation: Data Science Laboratory, ISI Foundation, 10126 Torino, Italy.
authors.forename: Ciro
authors.initials: C
authors.lastname: Cattuto
authors.affiliation: Aix Marseille Université, Université de Toulon, CNRS, CPT, UMR 7332, 13288 Marseille, France.|Data Science Laboratory, ISI Foundation, 10126 Torino, Italy.
authors.forename: Alain
authors.initials: A
authors.lastname: Barrat
mesh_terms: D000465:Algorithms; D003141:Communicable Diseases / Q000453:epidemiology* / Q000635:transmission; D016358:Contact Tracing; D006801:Humans; D008962:Models, Theoretical