Estimating the epidemic risk using non-uniformly sampled contact data

TL;DR

This paper develops methods to correct biases in epidemic risk estimates caused by non-uniform contact data sampling, improving accuracy especially in structured populations, with implications for better epidemic modeling.

Contribution

It introduces two surrogate data generation methods to mitigate bias from non-uniform sampling in contact networks, enhancing epidemic risk estimation accuracy.

Findings

Surrogate data improves epidemic risk estimates over raw sampled data.

The second method performs well in strongly structured populations.

Limitations arise in populations with weak group structures.

Abstract

Many datasets describing contacts in a population suffer from incompleteness due to population sampling and underreporting of contacts. Data-driven simulations of spreading processes using such incomplete data lead to an underestimation of the epidemic risk, and it is therefore important to devise methods to correct this bias. We focus here on a non-uniform sampling of the contacts between individuals, aimed at mimicking the results of diaries or surveys, and consider as case studies two datasets collected in different contexts. We show that using surrogate data built using a method developed in the case of uniform population sampling yields an improvement with respect to the use of the sampled data but is strongly limited by the underestimation of the link density in the sampled network. We put forward a second method to build surrogate data that assumes knowledge of the density of links within one of the groups forming the population. We show that it gives very good results when the population is strongly structured, and discuss its limitations in the case of a population with a weaker group structure. These limitations highlight the interest of measurements using wearable sensors able to yield accurate information on the structure and durations of contacts.