Research On Prediction Model Of Public Crisis Events And Its Application

With the frequent occurrence of crises in recent years, the crisis management has attracted a growing attention by governments. The crisis events’happening is not only a threat to human life and property safety, even affecting social stability. Crisis Management can be divide into four phases mitigation phase,preparation phase,reaction phases and recovery phases by the United States federal safety committee. Based on the mitigation phases of crisis management, we developed a Chinese Case Knowledge Base of Public Crisis Management using the advanced information technology means. The system collects the public crisis cases of4class grades with respect to natural disasters.accident disasters,public health and society security cases. It provides basic data and management platform for public crisis management teaching researchers,government emergency management personnel and social public.This paper takes crisis management as application background and uses Chinese Case Knowledge Base of Public Crisis Management as platform. The object of the study is to predict the public health events from the view of theory and practice in order to complete the analysis and warning of crisis events and help the government make better decisions.In this article.we get the grey markov combination prediction model based on the integration of the weighed markov chains model and GM(1,1) model.Based on the real incidence data of infectious disease which belongs to public health events.we compare the prediction precision of the three models respectively and analyze their advantages and disadvantages.Our experimental studies show that the grey markov prediction model could achieve the highest accuracy. Finally,we put them into case base and extend them to other infectious prediction application.