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.

中文翻译：

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识别在公共交通系统上传播疾病的极具影响力的旅行者

最近新型冠状病毒的爆发及其迅速传播凸显了了解人类流动性的重要性。封闭空间，例如公共交通工具（例如公共汽车和火车），为感染广泛而迅速地传播提供了合适的环境。因此，调查个人在公共交通系统上的移动模式和身体接触对于了解传染病爆发的驱动因素至关重要。例如，之前的工作探索了人类流动固有的重复模式对疾病传播的影响，但没有考虑其他维度，例如旅行距离或遭遇次数。在这里，我们同时考虑多个流动性维度，以揭示设计有效干预策略的关键信息。我们利用澳大利亚悉尼一个月的全市智能卡出行数据，从探索程度、出行距离和遭遇次数三个维度对公交车乘客进行分类。此外，我们模拟疾病在运输网络上的传播并追踪感染路径。我们详细调查了分类组之间的传播，同时改变了感染概率和病原体的悬浮时间。我们的结果表明，同时对多个维度的个体进行表征可以揭示不同乘客群体之间复杂的感染相互作用，当仅考虑单个维度时，这种相互作用将保持隐藏。根据特定的疾病特征，我们还确定了比其他群体更有影响力的群体，