Visualization Method for the Spreading Curve of COVID-19 in Universities using GNN

As the reopening of the university after the spread of COVID-19 on campus and we simulate and visualize the initial states spreading of COVID-19. In this research, we analyze and forecast the COVID-19 spreading curve of the resumption of in-person classes at university by the graph structure with the spread weight of edges based on each student's relation. Our approach is based on the effectiveness of three distancing strategies designed to keep the curve flat and aid make the spread of the COVID-19 controllable. By detecting the possibility of student relation based on three strategies, we can analyze the COVID-19 spreading curve by Graph Neural Network (GNN) and SIR model. In this article, we discuss two types of Open Group and Closed Group on university campuses and analyze face-to-face lectures, indoor social activities, and campus cafeterias. To verify the effectiveness of our two types of group, we simulated with the random infection curve by graph neural network model. At last, we visualized the COVID-19 spreading process and the results of diffusion prediction.