Estimating within-school contact networks to understand influenza transmission

TL;DR

This study develops a statistical model to estimate high school contact networks from survey data, revealing that static, dyad-independent ERGMs effectively capture social structures relevant to influenza transmission.

Contribution

The paper introduces a novel approach combining friendship data and contact surveys to accurately model within-school contact networks for epidemic modeling.

Findings

Friendship network structure can be modeled with dyad-independent ERGMs.

Contact behavior is adequately represented by a static network.

Static networks suffice for influenza transmission modeling.

Abstract

Many epidemic models approximate social contact behavior by assuming random mixing within mixing groups (e.g., homes, schools and workplaces). The effect of more realistic social network structure on estimates of epidemic parameters is an open area of exploration. We develop a detailed statistical model to estimate the social contact network within a high school using friendship network data and a survey of contact behavior. Our contact network model includes classroom structure, longer durations of contacts to friends than nonfriends and more frequent contacts with friends, based on reports in the contact survey. We performed simulation studies to explore which network structures are relevant to influenza transmission. These studies yield two key findings. First, we found that the friendship network structure important to the transmission process can be adequately represented by a dyad-independent exponential random graph model (ERGM). This means that individual-level sampled data is sufficient to characterize the entire friendship network. Second, we found that contact behavior was adequately represented by a static rather than dynamic contact network.