Research on the Forecast of the Spread of COVID-19

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.Immediate accessThis 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