ArticleTransportation research. Part C, Emerging technologies2021
Modeling epidemic spreading through public transit using time-varying encounter network.
Article in Transportation research. Part C, Emerging technologies, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed.
- Integrating epidemiological modeling and time-series forecasting to optimize pandemic patient allocation.International journal of health geographics · 2026Article
- Modelling the spread of infectious diseases in public transport systems under varying demand patterns and capacity constraints.Scientific reports · 2025Article
- Co-evolution model of traffic travel and disease transmission under limited resources.Scientific reports · 2025Article
- Model predicted human mobility explains COVID-19 transmission in urban space without behavioral data.Scientific reports · 2025Article
- Crowding on public transport using smart card data during the COVID-19 pandemic: New methodology and case study in Chile.Sustainable cities and society · 2023Article
- Assessing individual risk and the latent transmission of COVID-19 in a population with an interaction-driven temporal model.Scientific reports · 2023Article
- Understanding changing public transit travel patterns of urban visitors during COVID-19: A multi-stage study.Travel behaviour & society · 2023Article
- An evaluation framework for operational interventions on urban mass public transport during a pandemic.Scientific reports · 2023Article
- Quantitative resilience assessment of the network-level metro rail service's responses to the COVID-19 pandemic.Sustainable cities and society · 2023Article
- Effects of official information and rumor on resource-epidemic coevolution dynamics.Journal of King Saud University. Computer and information sciences · 2022Article
- Transmission and control of SARS-CoV-2 on ground public transport: A rapid review of the literature up to May 2021.Journal of transport & health · 2022Review
- Data analytics during pandemics: a transportation and location planning perspective.Annals of operations research · 2022Article
- Post pandemic fatigue: what are effective strategies?Scientific reports · 2022Article
- Transit use reduction following COVID-19: The effect of threat appraisal, proactive coping and institutional trust.Transportation research. Part A, Policy and practice · 2022Article
- Managing public transit during a pandemic: The trade-off between safety and mobility.Transportation research. Part C, Emerging technologies · 2022Article
- Assessing the impact of mobility on the incidence of COVID-19 in Dublin City.Sustainable cities and society · 2022Article
- A knowledge graph-based method for epidemic contact tracing in public transportation.Transportation research. Part C, Emerging technologies · 2022Article
- Impact of mobility on COVID-19 spread - A time series analysis.Transportation research interdisciplinary perspectives · 2022Article
- A topology-based evaluation of resilience on urban road networks against epidemic spread: Implications for COVID-19 responses.Frontiers in public health · 2022Article
- Social distancing in public transport: mobilising new technologies for demand management under the Covid-19 crisis.Transportation · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.