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Mobile Wireless Middleware, Operating Systems and Applications. 9th EAI International Conference, MOBILWARE 2020, Hohhot, China, July 11, 2020, Proceedings

Research Article

Measuring the Impact of Public Transit on the Transmission of Epidemics

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  • @INPROCEEDINGS{10.1007/978-3-030-62205-3_10,
        author={Yuan Bai and Qiuyang Huang and Zhanwei Du},
        title={Measuring the Impact of Public Transit on the Transmission of Epidemics},
        proceedings={Mobile Wireless Middleware, Operating Systems and Applications. 9th EAI International Conference, MOBILWARE 2020, Hohhot, China, July 11, 2020, Proceedings},
        proceedings_a={MOBILWARE},
        year={2020},
        month={11},
        keywords={Community structure Subway system Early prediction},
        doi={10.1007/978-3-030-62205-3_10}
    }
    
  • Yuan Bai
    Qiuyang Huang
    Zhanwei Du
    Year: 2020
    Measuring the Impact of Public Transit on the Transmission of Epidemics
    MOBILWARE
    Springer
    DOI: 10.1007/978-3-030-62205-3_10
Yuan Bai1, Qiuyang Huang2,*, Zhanwei Du1
  • 1: Department of Integrative Biology, University of Texas at Austin
  • 2: College of Computer Science and Technology, Jilin University
*Contact email: huangqy17@mails.jlu.edu.cn

Abstract

In many developing countries, public transit plays an important role in daily life. However, few existing methods have considered the influence of public transit in their models. In this work, we present a dual-perspective view of the epidemic spreading process of the individual that involves both contamination in places (such as work places and homes) and public transit (such as buses and trains). In more detail, we consider a group of individuals who travel to some places using public transit, and introduce public transit into the epidemic spreading process. Our simulation results suggest that individuals with a high public transit trip contribution rate will increase the volume of infectious people when an infectious disease outbreak occurs by affecting the social network through the public transit trip contribution rate.

Keywords
Community structure Subway system Early prediction
Published
2020-11-05
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-62205-3_10
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