  Understanding the spread of SARS-CoV-2 has been one of the most pressing
problems of the recent past. Network models present a potent approach to
studying such spreading phenomena because of their ability to represent complex
social interactions. While previous studies have shown that network centrality
measures are generally able to identify influential spreaders in a susceptible
population, it is not yet known if they can also be used to predict infection
risks. However, information about infection risks at the individual level is
vital for the design of targeted interventions. Here, we use large-scale
administrative data from the Netherlands to study whether centrality measures
can predict the risk and timing of infections with COVID-19-like diseases. We
investigate this issue leveraging the framework of multi-layer networks, which
accounts for interactions taking place in different contexts, such as
workplaces, households and schools. In epidemic models simulated on real-world
network data from over one million individuals, we find that existing
centrality measures offer good predictions of relative infection risks, and are
correlated with the timing of individual infections. We however find no
association between centrality measures and real SARS-CoV-2 test data, which
indicates that population-scale network data alone cannot aid predictions of
virus transmission.
