Ebola virus is a deadly infectious disease, which seriously endangers human life and safety. Ac-curate prediction of the development trend of an infectious disease is of significant importance for the timely allocation and mobilization of relevant medical resources, developing reasonable strategies, as well as solving the problem of experience dependence in emergency treatment of the health sector. In order to effectively deal with the spread of the virus, this paper introduces three different methods to establish the prediction model of the Ebola virus transmission process, i.e. the probability model, the disease development course model and the system dynamics model. Through data mining of the time series variables in the disease spreading history, we can establish our predicting model based on the spreading rules of Ebola epidemic disease. Through the Ebola epidemic prediction model, we can make predictions on the future spread of the virus and simulate different control strategies under different scenarios to explore for the most effective measures. Therefore, through this research, we can provide effective technical support and suggestions for epidemic countries in Ebola disease management and to improve the public health emergency response ability. In addition, the proposed models of this paper also take some realistic factors, such as the difficulty degree of drug distribution and the medical service level in different epidemic areas into account to make improvements on the original model, which help the models to be more realistic in the simulation of the Ebola virus spreading process.
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