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    Research on the Forecast of the Spread of COVID-19

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    Author
    Guo, Lihao
    Yang, Yuxin
    Affiliation
    James E Rogers College of Law, The University of Arizona
    Issue Date
    2021-07-20
    
    Metadata
    Show full item record
    Publisher
    ACM
    Citation
    Guo, L., & Yang, Y. (2021). Research on the Forecast of the Spread of COVID-19. ACM International Conference Proceeding Series, 47–51.
    Journal
    ACM International Conference Proceeding Series
    Rights
    © 2021 Association for Computing Machinery.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    With the spreading of COVID-19, various existing machine learning frameworks can be adopted to effectively control the epidemic to help research and predict the spread of the virus before the large-scale application of vaccines. Based on the spatiotemporal graph neural network and mobility data, this paper attempts to offer a novel prediction by building a high-resolution graph with the characteristics such as willingness to wear masks, daily infection, and daily death. This model is different from the time series prediction model. The method learns from the multivariate spatiotemporal graph, the nodes represent the region with daily confirmed cases and death, and edges represent the inter-regional contacts based on mobility. Simultaneously, the transmission model is built by a time margin as the characteristic of the time change. This paper builds the COVID-19 model by using STGNNs and tries to predict and verify the virus's infection. Finally, the model has an absolute Pearson Correlation of 0.9735, far from the expected value of 0.998. The predicted value on the first and second day is close to the real situation, while the value gradually deviates from the actual situation after the second day. It still shows that the graph neural network uses much temporal and spatial information to enable the model to learn complex dynamics. In the future, the model can be improved by tuning hyper-parameter such as modulation numbers of convolution, or construction of graphs that suitable for smaller individuals such as institutions, buildings, and houses, as well as assigning more features to each node. This experiment demonstrates the powerful combination of deep learning and graph neural networks to study the spread and evolution of COVID-19.
    Note
    Immediate access
    DOI
    10.1145/3460238.3460246
    Version
    Final accepted manuscript
    ae974a485f413a2113503eed53cd6c53
    10.1145/3460238.3460246
    Scopus Count
    Collections
    UA Faculty Publications

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