Abstract

The prevention and treatment of epidemic is always an urgent problem faced by the human being. Due to the special space structure, huge passenger flow and great people mobility, the subway lines have become the areas with high epidemic transmission risks. However, there is no recent study related to epidemic transmission in the subway network on urban-scale. In this article, from the perspective of big data, we study the transmission risk of epidemic in Beijing subway network by using urban subway mobility data. By reintegrating and mining the urban subway mobility data, we preliminary assess the transmission risk in the subway lines from the passenger behaviors, station features, route features and individual case on the basis of subway network structure. This study has certain practical significance for the early stage of epidemic tracking and prevention.

Highlights

  • Epidemic has always been a serious threat to human health

  • The prevention and treatment of epidemic is always an urgent problem faced by the human being

  • There is no recent study related to the epidemic transmission in the subway network on urban-scale

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Summary

Introduction

Epidemic has always been a serious threat to human health. The prevention and treatment of epidemic is always an urgent problem faced by the human being. The modeling and assessment of this process can help us to understand the mechanism of the spread of epidemic and provide corresponding basis of epidemic analysis, simulation and interference [1]. The researches on the spread of epidemic are being deepened and refined [2]-[11]. (2015) A Preliminary Study on Spatial Spread Risk of Epidemics by Analyzing the Urban Subway Mobility Data. The transmission of epidemic in transportation network is gradually concerned by researchers [12]. The researches of transportation network are basically aviation network based on modeling method. There is no recent study related to the epidemic transmission in the subway network on urban-scale. It is meaningful and valuable to reconstruct the subway flow information and build risk assessment of different routes and stations by using the idea of big data

Data and Method
Method
Passenger Behavior
Station Risk Assessment
Route Risk Assessment
Individual Case Tracking
Conclusion
Full Text
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