Prediction of Changes in Hospitalized Patients with Newly Emerging Infectious Diseases based on SEIR Model
Objective To explore the development pattern of newly emerging major infectious diseases,to predict the trend of changes in hospitalized patients,and to provide scientific basis for responding to public health emergen-cies,assessing medical manpower needs,and formulating epidemic prevention and control strategies in the fu-ture.Methods Based on the theory of the SEIR model(sustainable exposed affected and removed,SEIR),combined with the prevention and control policies of COVID-19 in Sichuan province,a dynamic model of infectious diseases in three stages of early warning,outbreak and recovery in the process of epidemic transmission was estab-lished.According to the real-time data of COVID-19 released by Sichuan Health Commission,the daily newly con-firmed cases,cumulative confirmed cases,daily existing suspected cases,cumulative discharged cases and death cases of COVID-19 patients from January 21,2020 to March 25,2020 were selected,and the least squares optimization al-gorithm was used to solve the model parameters,output the model fitting results,and evaluate the feasibility of the model.Results The overall trend of the model fitting curve was basically consistent with the actual reference curve.The average absolute percentage error during the early warning stage was 30.06％,and the root mean square error was 17.840 4.The average absolute percentage error during the outbreak stage was 10.14％,and the root mean square error was 66.845 2.The root mean square error during the recovery phase was 16.508 2.Conclusion The multi-stage SEIR model established in this article has a good overall fitting effect,which can be used to predict the trend of changes in the number of newly diagnosed major infectious disease inpatients.
Emerging infectious diseasesDynamic model of infectious diseasesInpatientCOVID-19
万方数据
维普
