Epidemic Dynamics Analysis of COVID-19 Using a Modified SEIR Model with Symptom Classifications

The prevention and control of COVID-19 epidemic is a great challenge for human beings today. In the battle against COVID-19, the hierarchical treatment measures based on symptom classifications have proved to be a particularly effective way to deal with the large-scale epidemic in the absence of adequate medical resources. This paper deals with the epidemic dynamic analyses of the COVID-19 based on a modified SEIR model with different symptoms. First, by taking symptom classifications and hierarchical treatments of patients into account, a modified SEIR model is established. Then, the proposed differential equations model is solved by using Runge-Kutta methods, and the parameters herein are estimated by least square principle based on the data released by the National Health Commission. Simulation results of the model show that the introduction of symptom classifications in the SEIR model can not only improve the fitting accuracy, but also precisely describe the evolution rules and mutual transfer rules of patients with different symptoms. The model can provide theoretical support for decision-making of the corresponding government departments, especially for the construction of mobile cabin hospitals and the reasonable preparation of important epidemic prevention resources.