Compensating for population sampling in simulations of epidemic spread on temporal contact networks
Génois, Mathieu; Vestergaard, Christian L; Cattuto, Ciro; Barrat, Alain
- 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 correct this bias and obtain an accurate 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 that the statistical information contained in the resampled data allows us to build surrogate versions of the unknown contacts and that simulations of epidemic processes using these surrogate data sets yield good estimates of the outcome of simulations performed using the complete data set. We discuss limitations and potential improvements of our method.
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- Nature Communications