Contact network modelling for the infectious disease spread predictions
CoronaVirus 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...
| প্রধান লেখক: | |
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| অন্যান্য লেখক: | |
| বিন্যাস: | Final Year Project (FYP) |
| ভাষা: | English |
| প্রকাশিত: |
Nanyang Technological University
2022
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | https://hdl.handle.net/10356/156637 |
| _version_ | 1826129060087988224 |
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| author | Liu, Wanrui |
| author2 | Cai Wentong |
| author_facet | Cai Wentong Liu, Wanrui |
| author_sort | Liu, Wanrui |
| collection | NTU |
| description | CoronaVirus 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. |
| first_indexed | 2024-10-01T07:34:33Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/156637 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:34:33Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1566372022-04-21T07:38:37Z Contact network modelling for the infectious disease spread predictions Liu, Wanrui Cai Wentong School of Computer Science and Engineering Kwak Jaeyoung ASWTCAI@ntu.edu.sg Engineering::Computer science and engineering CoronaVirus 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. Bachelor of Engineering (Computer Science) 2022-04-21T07:38:37Z 2022-04-21T07:38:37Z 2022 Final Year Project (FYP) Liu, W. (2022). Contact network modelling for the infectious disease spread predictions. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156637 https://hdl.handle.net/10356/156637 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Liu, Wanrui Contact network modelling for the infectious disease spread predictions |
| title | Contact network modelling for the infectious disease spread predictions |
| title_full | Contact network modelling for the infectious disease spread predictions |
| title_fullStr | Contact network modelling for the infectious disease spread predictions |
| title_full_unstemmed | Contact network modelling for the infectious disease spread predictions |
| title_short | Contact network modelling for the infectious disease spread predictions |
| title_sort | contact network modelling for the infectious disease spread predictions |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/156637 |
| work_keys_str_mv | AT liuwanrui contactnetworkmodellingfortheinfectiousdiseasespreadpredictions |