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Passenger contact in public transit (PT) networks can be a key mediate in thespreading of infectious diseases. This paper proposes a time-varying weightedPT encounter network to model the spreading of infectious diseases through thePT systems. Social activity contacts at both local and global levels are alsoconsidered. We select the epidemiological characteristics of coronavirusdisease 2019 (COVID-19) as a case study along with smart card data fromSingapore to illustrate the model at the metropolitan level. A scalable andlightweight theoretical framework is derived to capture the time-varying andheterogeneous network structures, which enables to solve the problem at thewhole population level with low computational costs. Different control policiesfrom both the public health side and the transportation side are evaluated. Wefind that people's preventative behavior is one of the most effective measuresto control the spreading of epidemics. From the transportation side, partialclosure of bus routes helps to slow down but cannot fully contain the spreadingof epidemics. Identifying "influential passengers" using the smart card dataand isolating them at an early stage can also effectively reduce the epidemicspreading.

Modeling Epidemic Spreading through Public Transit using Time-Varying Encounter Network