| Summary: | 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.
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