Outbreaks of infectious diseases present a global threat to human health and are considered a major health-care challenge. One major driver for the rapid spatial spread of diseases is human mobility. In particular, the travel patterns of individuals determine their spreading potential to a great extent. These travel behaviors can be captured and modelled using novel location-based data sources, e.g., smart travel cards, social media, etc. Previous studies have shown that individuals who cannot be characterized by their most frequently visited locations spread diseases farther and faster; however, these studies are based on GPS data and mobile call records which have position uncertainty and do not capture explicit contacts. It is unclear if the same conclusions hold for large scale real-world transport networks. In this paper, we investigate how mobility patterns impact disease spread in a large-scale public transit network of empirical data traces. In contrast to previous findings, our results reveal that individuals with mobility patterns characterized by their most frequently visited locations and who typically travel large distances pose the highest spreading risk.

中文翻译：

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移动模式如何推动疾病传播：使用公共交通乘客卡旅行数据的案例研究

传染病的爆发对人类健康构成了全球威胁，被认为是一项重大的卫生保健挑战。疾病快速空间传播的一个主要驱动因素是人类的流动性。特别是，个人的出行方式在很大程度上决定了他们的传播潜力。可以使用新的基于位置的数据源（例如智能旅行卡、社交媒体等）来捕获和建模这些旅行行为。先前的研究表明，无法以最常访问的位置为特征的个人将疾病传播得更远更快；然而，这些研究是基于 GPS 数据和移动通话记录，这些数据具有位置不确定性并且没有捕捉到明确的联系。目前尚不清楚同样的结论是否适用于大规模的现实世界交通网络。在本文中，我们研究了移动模式如何在经验数据跟踪的大规模公共交通网络中影响疾病传播。与之前的研究结果相反，我们的结果表明，具有以最常访问的地点为特征且通常长途旅行的流动模式的个人构成了最高的传播风险。