A Staging Prediction Model for COVID-19 Pandemic Under Strong Public Health Interventions

With the extensive implementation of the strong public health interventions in China, many models proposed to predict COVID-19 epidemic are no longer applicable to the current epidemic development. In this paper, a COVID-19 prediction method is proposed based on a staging SEITR model with consideration of strong public health interventions in China. The method simulates preventive and control measures such as mass nucleic acid testing and quarantine of close contacts by introducing the role of Isolates and the transformation of Exposed to Isolated. The experimental evaluation uses real epidemic data from six cities including Nanjing, Yangzhou, and etc. The accuracy of prediction for total number of infections reaches 95.8% with the data of the first 15 days of the outbreak. In addition, the prediction accuracy of the end of the pandemic is 95.07%. These show that the proposed method can effectively predict the course of the epidemic and it is practical for relevant departments to formulate reasonable prevention and control measures.