A Retrospective Study of COVID-19 Based on A Dual-Stage Model

This study proposed initial stage SEI1I2I3RD and final stage SEQ I1I2I3RD models to deeply analyze the transmission dynamics of COVID-19 at different stages and propose scientific prevention and control strategies. The initial stage SE I1I2I3RD model divides infected individuals into mild, moderate, and severe cases during the early stage of the epidemic, effectively predicting the number of new infections and cumulative recoveries. The final stage SEQ I1I2I3RD model builds on the initial stage SE I1I2I3RD model by adding a quarantine compartment, which reflects the impact of nucleic acid testing and quarantine measures on epidemic control. By introducing dynamic parameters and nonlinear processing, the final stage SEQ I1I2I3RD model significantly improves prediction accuracy. The study demonstrates that increasing the isolation rate, reducing the daily contact rate of infected persons, and improving the recovery rate of mildly infected persons are effective control strategies. Based on the model's predictions, policy recommendations such as enhancing public health education, strictly implementing isolation measures, restricting population movement, and optimizing medical resource allocation are proposed to effectively control the spread of the epidemic, reduce the burden on the healthcare system, and create favorable conditions for economic recovery.