Modeling epidemic spreading through public transit using time-varying encounter network. (January 2021)

• Record Type: Journal Article
• Authors: Mo, Baichuan; Feng, Kairui; Shen, Yu; Tam, Clarence; Li, Daqing; Yin, Yafeng; Zhao, Jinhua
• Is Part Of: Transportation research. Volume 122(2021)
• Publication Date: 2021-01
• Subjects: Public transit encounter -- Epidemics -- Time-varying complex network -- COVID-19; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2020.102893
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: A time-varying weighted encounter network to model epidemic spreading in PT. A scalable and lightweight theoretical framework to solve the problem. Various public health and transportation-related control policies are evaluated. Partial closure of bus routes cannot fully contain the spreading of epidemics. Isolating influential passengers" at an early stage can reduce the spreading. 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.