  Outbreaks of infectious diseases present a global threat to human health and
are considered a major health-care challenge. One major driver for the rapid
spatial spread of diseases is human mobility. In particular, the travel
patterns of individuals determine their spreading potential to a great extent.
These travel behaviors can be captured and modelled using novel location-based
data sources, e.g., smart travel cards, social media, etc. Previous studies
have shown that individuals who cannot be characterized by their most
frequently visited locations spread diseases farther and faster; however, these
studies are based on GPS data and mobile call records which have position
uncertainty and do not capture explicit contacts. It is unclear if the same
conclusions hold for large scale real-world transport networks. In this paper,
we investigate how mobility patterns impact disease spread in a large-scale
public transit network of empirical data traces. In contrast to previous
findings, our results reveal that individuals with mobility patterns
characterized by their most frequently visited locations and who typically
travel large distances pose the highest spreading risk.
