  The recent outbreak of a novel coronavirus and its rapid spread underlines
the importance of understanding human mobility. Enclosed spaces, such as public
transport vehicles (e.g. buses and trains), offer a suitable environment for
infections to spread widely and quickly. Investigating the movement patterns
and the physical encounters of individuals on public transit systems is thus
critical to understand the drivers of infectious disease outbreaks. For
instance previous work has explored the impact of recurring patterns inherent
in human mobility on disease spread, but has not considered other dimensions
such as the distance travelled or the number of encounters. Here, we consider
multiple mobility dimensions simultaneously to uncover critical information for
the design of effective intervention strategies. We use one month of citywide
smart card travel data collected in Sydney, Australia to classify bus
passengers along three dimensions, namely the degree of exploration, the
distance travelled and the number of encounters. Additionally, we simulate
disease spread on the transport network and trace the infection paths. We
investigate in detail the transmissions between the classified groups while
varying the infection probability and the suspension time of pathogens. Our
results show that characterizing individuals along multiple dimensions
simultaneously uncovers a complex infection interplay between the different
groups of passengers, that would remain hidden when considering only a single
dimension. We also identify groups that are more influential than others given
specific disease characteristics, which can guide containment and vaccination
efforts.
