Investigation of traffic-driven epidemic spreading by taxi trip data

Abstract

Urban transportation systems account for tremendous population movements, which can also trigger epidemic outbreaks. This paper investigates the coupling of epidemic spreading and human mobility via taxi-trip data analysis and the Markov chain approach, and proposes targeted epidemic prevention measures. Significant variations in travel patterns are observed across different taxi zones within the city, and these disparities have a substantial impact on epidemic dynamics. First, the probability of taxi drivers and passengers traveling among taxi zones is obtained from empirical data. Abstracting human travel pattern as Markov process, a traffic-driven epidemic spreading model is established. Considering the impact of trip probability on disease spreading, the model can effectively reproduce the outbreak of COVID-19 in New York City, with correct features in different boroughs. Quantitative parameters are derived to indicate the influence of taxi zones and origin-destination trips on epidemic transmission. Applying prevention measures to a small number of important zones or key origin-destination trips in the early stage of spreading, the scale of epidemic outbreaks can be significantly reduced. This research offers insights for suppressing epidemic spread in densely populated metropolitan areas, with the potential to benefit policy efforts.