Construction of Big Data Epidemic Forecast and Propagation Model and Analysis of Risk Visualization Trend

Studying the spread of infectious diseases and predicting the development trend of infectious diseases is an important aspect of studying infectious diseases. It is the basis for government departments and health institutions to formulate corresponding control measures. In this paper, according to the characteristics of epidemic spread, a differential equation model with time-delay terms is established. Based on the traditional SIR model, the model newly added free carriers. These people are the source of the spread of the epidemic. You can control the spread of the epidemic by controlling the free carriers. The simulation proves the rationality of the model. This model can achieve a better fitting effect on the development trend of the number of outbreaks in each province, which shows the importance of the closure and isolation measures for the prevention and control of the outbreak, and also verifies the secondary propagation model found in the visualization stage. The basic Kalman filter is used to predict the epidemic situation, and the stability of the filtering method is analyzed according to the Kalman filter stability criterion. The analysis results show that the filtering method is stable. Through simulation, it is found that the prediction results of the two methods used to predict the epidemic situation are in good agreement with the actual data of the epidemic situation, and have high prediction accuracy, which provides a new idea for epidemic situation prediction.