Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorLiu, Wanruien_US
dc.identifier.citationLiu, W. (2022). Contact network modelling for the infectious disease spread predictions. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractCoronaVirus Disease 2019 (COVID-19), due to its extremely high infectivity, has been spreading rapidly around the world, impacting socioeconomic developments as well as the daily lives of billions of people. As such, it will be meaningful to be able to model and study the transmission of COVID-19 in crowded spaces and the effectiveness of safety measures such as wearing masks properly and social distancing. In the past decade, various computer models for crowd movement have been developed and can be used to identify health and safety issues. State-of-the-art models that simulate the spread of epidemics operate on a population level, but the collection of fine-scale data might enable the development of models for epidemics that operate on a microscopic scale, similar to models for crowd movement. This paper explores the data-driven modelling framework to construct a network model based on real-world video data taken from NYC Union Station, to simulate the spread of COVID-19 in a subway station. The trajectory data of the pedestrians caught on the video were integrated into a contact network to study the potential transmission that may occur between them. The experimental results show that the proposed model in this paper efficiently simulates how the virus spread in the dense crowd. Furthermore, the model also shows that self-protection measures, such as wearing masks and staying a safe distance from others consciously, during the epidemic can effectively reduce the prevalence, and finally lower the risk of COVID-19 transmission in public areas.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleContact network modelling for the infectious disease spread predictionsen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorCai Wentongen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.supervisor2Kwak Jaeyoungen_US
item.fulltextWith Fulltext-
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
Files in This Item:
File Description SizeFormat 
  Restricted Access
2.22 MBAdobe PDFView/Open

Page view(s)

Updated on Jun 30, 2022


Updated on Jun 30, 2022

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.