Research On Prediction And Process Monitoring Of COVID-19 Epidemic

As the Novel Coronavirus pandemic spread around the world,it gradually evolved into a global public crisis,result in a serious threat to the lives of all mankind.Although the COVID-19 epidemic has been brought under control in China,there are still small outbreaks,which leads to a severe challenge to the epidemic prevention authorities.There has been a lot of research in the field of epidemic early warning.If there is a study that can give early warning of the outbreak of COVID-19,it will have important theoretical value and practical significance.Therefore,this thesis takes the early warning of COVID-19 outbreak as the research object,and discusses the feasibility and accuracy of statistical process control method based on the prediction results of ARIMA model.In this thesis,ARIMA model and EWMA control chart are introduced.Then,the modeling steps of the ARIMA model were explained.Next,the ARIMA model based on the data of COVID-19 in China was established,and the most accurate ARIMA model for predicting the number of confirmed COVID-19 cases was obtained through various evaluation indicators.Then EWMA control chart was used to monitor the prediction results of ARIMA model to judge the outbreak and possible outbreak date.It will achieve the effect of pre-warning the outbreak of the epidemic.Finally,the feasibility and accuracy of this method are verified by actual epidemic data.In this thesis,ARIMA model and EWMA control chart are combined.Monitoring the prediction results of ARIMA model with EWMA control chart,so as to give early warning to the outbreak of the epidemic.It has enriched research on COVID-19 monitoring,and helped epidemic prevention departments take measures to reduce the harm caused by the epidemic.