3 research outputs found
Identifying highly influential travellers for spreading disease on a public transport system
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.Comment: 10 pages, 10 figures and 1 table. To be published in the 2020 21st
IEEE International Symposium on A World of Wireless, Mobile and Multimedia
Networks (IEEE WOWMOM 2020) conference program and the proceeding
Identifying Highly Influential Travellers for Spreading Disease on a Public Transport System
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, wes imulate 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. </p