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

TL;DR

This study investigates how spatially constrained sampling of human contact networks affects the accuracy of epidemic risk estimation, revealing biases and conditions under which estimates are reliable.

Contribution

It introduces an analysis of the impact of spatial sampling on epidemic threshold estimation using real and synthetic contact data.

Findings

Spatial sampling underestimates epidemic risk compared to complete data.

Bias decreases with higher contact recording fractions.

The effect depends on the interaction between population and disease dynamics.

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 susceptible-infectious-recovered-susceptible 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 nonlinearly 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.