Identifying Highly Influential Travellers for Spreading Disease on a Public Transport System

Abstract

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.

A global perspective on the intrinsic dimensionality of COVID-19 data

“…Data science methods for high-dimensionality datasets have been utilised and explored in multiple contexts to aid decision-making and analysis during the COVID-19 pandemic. For example, citywide smart card travel data has been utilised in Sydney, Australia, to cluster passenger types along multiple mobility dimensions and develop intervention strategies for disease spread 3 . Similarly, manifold learning techniques have been applied to cell-phone mobility data in the United States during the COVID-19 pandemic, distinguishing mobility trends in multiple geographic regions and demographics 4 .…”

On the intrinsic dimensionality of Covid-19 data: a global perspective

“…Data science methods for high-dimensionality datasets have been utilised and explored in multiple contexts to aid decision-making and analysis during the Covid-19 pandemic. For example, citywide smart card travel data in Sydney, Australia, has been utilised to cluster passenger types along multiple mobility dimensions and develop intervention strategies for disease spread (Shoghri et al, 2020). Similarly, manifold learning techniques have been applied to cell-phone mobility data in the United States during the Covid-19 pandemic, distinguishing mobility trends in multiple geographic regions and demographics (Levin et al, 2020).…”

Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic

“…A variety of modelling strategies have been applied. Techniques include: empirical approaches such as phenomenological growth curves [29]; data-driven, statistical approaches using non-linear autoregressive models [31]; and mechanistic models based on epidemiological theory [32] with various extensions [33,34].…”