Abstract
Improved mobility not only contributes to more intensive human activities but also facilitates the spread of communicable disease, thus constituting a major threat to billions of urban commuters. In this study, we present a multi-city investigation of communicable diseases percolating among metro travelers. We use smart card data from three megacities in China to construct individual-level contact networks, based on which the spread of disease is modeled and studied. We observe that, though differing in urban forms, network layouts, and mobility patterns, the metro systems of the three cities share similar contact network structures. This motivates us to develop a universal generation model that captures the distributions of the number of contacts as well as the contact duration among individual travelers. This model explains how the structural properties of the metro contact network are associated with the risk level of communicable diseases. Our results highlight the vulnerability of urban mass transit systems during disease outbreaks and suggest important planning and operation strategies for mitigating the risk of communicable diseases.
Highlights
To gain insights into how travelers come in contact with others during travel, we develop a simulation model based on the observed metro network layout, demand profile, and mobility patterns
The simulation constructs high-resolution metro contact networks (MCNs) by first sampling passenger arrivals at each metro station and their trip destinations, calculating if two individuals will come into contact based on their trip profiles, and assigning expected contact duration between each pair of individuals (The detailed description of the simulation is presented in Methods)
This explains the lack of high degree nodes in the contact network as compared to the scale-free network with the same number of nodes and average degree, since the probability of having large k in a random network diminishes faster than exponential
Summary
The availability of data motivated initial attempts on exploring the risk of infectious diseases in public transportation systems, where the transit smart card data and passenger demand data are used to restore individual trip sequences, construct potential encounter networks among travelers and simulate the outbreaks of infectious diseases on the encounter networks[37,38,39] These studies connected the disease percolation process with either mobility networks or contact networks, and the data and the networks are mainly used to deliver descriptive and predictive analyses. We find that there is a large number of travelers with travel time under 50 minutes, and the number of travelers decays exponentially with increasing trip length This finding holds true across all three cities, with Shanghai having a lower decay rate (16.59min) and the decay rates for Guangzhou (13.78min) and Shenzhen (13.43min) being almost identical. As physical encounters are driven by human mobility, this motivates us to investigate the possible existence of scaling laws for the contact patterns in public mass transit networks, as the results of the universal mobility patterns
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