Prediction and Analysis of Infectious Diseases Based on M-SIR Model

The epidemic of infectious diseases has become a major problem threatening the world public health, and the dynamic models of virus spreading are widely used for epidemic tracking and prediction. The existing dynamic models do not consider the synergistic effects of population migration factors and changes in transmission rates on diseases. Therefore, based on the SIR (Susceptible-Infectious-Recovered) model, the time-dependent M-SIR (Migration-Susceptible-Infectious-Recovered) model was proposed by introducing the population migration (Migration) factor. Meanwhile, introducing the machine learning LightGBM (Light Gradient Boosting Machine) method to track the infection rate and recovery rate, and explored the impact of cross-regional population movement and prevention and control measures on the development of the epidemic. Take the new crown epidemic as an example, firstly, the data of population migration and epidemic spread were statistically analyzed to monitor the relationship between population mobility and epidemic development. Then, the m-sir model is used to predict the infected cases and removed cases in Beijing and Shanghai. Through comparative analysis with the SIR model, the prediction accuracy of the model has been greatly improved. At the same time, the development trend of the epidemic situation in related cities before and after control is explored, which can provide some theoretical support for future epidemic prediction and control decisions.