Modeling epidemic spreading through public transit using time-varying encounter network
Name
2004.04602.pdf
Description
Accepted version
Size
6.06 MB
Format
Adobe PDF
Checksum (MD5)
0dd644dbcb51356198a883944ff571c0
Author(s)
Mo, Baichuan
Feng, Kairui
Shen, Yu
Tam, Clarence
Li, Daqing
Yin, Yafeng
Zhao, Jinhua
Date Issued
January 2021
Journal
Transportation Research Part C: Emerging Technologies
Publisher
Elsevier BV
Citation
Mo, Baichuan et al. "Modeling epidemic spreading through public transit using time-varying encounter network." Transportation Research Part C: Emerging Technologies 122 (January 2021): 102893 © 2020 Elsevier Ltd
Version
Author's final manuscript
Abstract
Passenger contact in public transit (PT) networks can be a key mediate in the spreading of infectious diseases. This paper proposes a time-varying weighted PT encounter network to model the spreading of infectious diseases through the PT systems. Social activity contacts at both local and global levels are also considered. We select the epidemiological characteristics of coronavirus disease 2019 (COVID-19) as a case study along with smart card data from Singapore to illustrate the model at the metropolitan level. A scalable and lightweight theoretical framework is derived to capture the time-varying and heterogeneous network structures, which enables to solve the problem at the whole population level with low computational costs. Different control policies from both the public health side and the transportation side are evaluated. We find that people's preventative behavior is one of the most effective measures to control the spreading of epidemics. From the transportation side, partial closure of bus routes helps to slow down but cannot fully contain the spreading of epidemics. Identifying “influential passengers” using the smart card data and isolating them at an early stage can also effectively reduce the epidemic spreading.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.trc.2020.102893
