Compensating for population sampling in simulations of epidemic spread on temporal contact networks

TL;DR

This paper introduces a systematic method to correct for sampling bias in contact network data, improving epidemic risk estimation by reconstructing missing contact information through surrogate data models.

Contribution

It presents a novel approach to compensate for incomplete population sampling in high-resolution contact data, enhancing the accuracy of epidemic spread simulations.

Findings

Reconstructed contact data improves epidemic risk estimates.

Simulation results closely match outcomes from complete data.

Method reduces underestimation of epidemic spread due to sampling bias.

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.