On 12 December 2019, a novel coronavirus disease, named COVID-19, began to spread around the world from Wuhan, China. It is useful and urgent to consider the future trend of this outbreak. We establish the 4+1 penta-group model to predict the development of the COVID-19 outbreak. In this model, we use the collected data to calibrate the parameters, and let the recovery rate and mortality change according to the actual situation. Furthermore, we propose the BAT model, which is composed of three parts: simulation of the return rush (Back), analytic hierarchy process (AHP) method, and technique for order preference by similarity to an ideal solution (TOPSIS) method, to figure out the best return date for university students. We also discuss the impacts of some factors that may occur in the future, such as secondary infection, emergence of effective drugs, and population flow from Korea to China. Key words: epidemic dynamics model, nonlinear least squares, analytic hierarchy process (AHP), technique for order preference by similarity to an ideal solution (TOPSIS) CLC number: R 511 Document code: A

The outbreak of coronavirus disease 2019 in Wuhan has aroused widespread concern and attention from all over the world. Many articles have predicted the development of the epidemic. Most of them only use very basic SEIR model without considering the real situation. In this paper, we build a model called e-ISHR model based on SEIR model. Then we add hospital system and time delay system into the original model to simulate the spread of COVID-19 better. Besides, in order to take the government's control and people's awareness into consideration, we change our e-ISHR model into a 3-staged model which effectively shows the impact of these factors on the spread of the disease. By using this e-ISHR model, we fit and predict the number of confirmed cases in Wuhan and China except Hubei. We also change some of parameters in our model. The results indicate the importance of isolation and increasing the number of beds in hospital.

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