7 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
Indirect interactions influence contact network structure and diffusion dynamics
Interaction patterns at the individual level influence the behaviour of diffusion over contact networks. Most of the current diffusion models only consider direct interactions, capable of transferring infectious items among individuals, to build transmission networks of diffusion. However, delayed indirect interactions, where a susceptible individual interacts with infectious items after the infected individual has left the interaction space, can also cause transmission events. We define a diffusion model called the same place different time transmission (SPDT)-based diffusion that considers transmission links for these indirect interactions. Our SPDT model changes the network dynamics where the connectivity among individuals varies with the decay rates of link infectivity. We investigate SPDT diffusion behaviours by simulating airborne disease spreading on data-driven contact networks. The SPDT model significantly increases diffusion dynamics with a high rate of disease transmission. By making the underlying connectivity denser and stronger due to the inclusion of indirect transmissions, SPDT models are more realistic than same place same time transmission (SPST)-based models for the study of various airborne disease outbreaks. Importantly, we also find that the diffusion dynamics including indirect links are not reproducible by the current SPST models based on direct links, even if both SPDT and SPST networks assume the same underlying connectivity. This is because the transmission dynamics of indirect links are different from those of direct links. These outcomes highlight the importance of the indirect links for predicting outbreaks of airborne diseases.</p
Data from: Indirect interactions influence contact network structure and diffusion dynamics
Interaction patterns at the individual level influence the behaviour of diffusion over contact networks. Most of the current diffusion models only consider direct interactions, capable of transferring infectious items among individuals, to build transmission networks of diffusion. However, delayed indirect interactions, where a susceptible individual interacts with infectious items after the infected individual has left the interaction space, can also cause transmission events. We define a diffusion model called the same place different time transmission (SPDT) based diffusion that considers transmission links for these indirect interactions. Our SPDT model changes the network dynamics where the connectivity among individuals varies with the decay rates of link infectivity. We investigate SPDT diffusion behaviours by simulating airborne disease spreading on data-driven contact networks. The SPDT model significantly increases diffusion dynamics with a high rate of disease transmission. By making the underlying connectivity denser and stronger due to the inclusion of indirect transmissions, SPDT models are more realistic than SPST models for the study of various airborne diseases outbreaks. Importantly, we also find that the diffusion dynamics including indirect links are not reproducible by the current SPST models based on direct links, even if both SPDT and SPST networks assume the same underlying connectivity. This is because the transmission dynamics of indirect links are different from those of direct links. These outcomes highlight the importance of the indirect links for predicting outbreaks of airborne diseases