Application of COVID-19 pneumonia diffusion data to predict epidemic situation

Abstract Objective To evaluate novel coronavirus pneumonia cases by establishing the mathematical model of the number of confirmed cases daily, and to assess the current situation and development of the epidemic situation, so as to provide a digital basis for decision-making. Methods The number of newly confirmed covid-19 cases per day was taken as the research object, and the seven-day average value ( M ) and the sequential value ( R ) of M were calculated to study the occurrence and development of covid-19 epidemic through the analysis of charts and data. Results M reflected the current situation of epidemic development; R reflected the current level of infection and the trend of epidemic development. Conclusion The current data can be used to evaluate the number of people who have been infected, and when R < 1, the peak of epidemic can be predicted. Preface In December 2019, a number of cases of pneumonia with unknown causes were found in some hospitals in Wuhan, Hubei province, China. On 11 March 2020, the director-general of the world health organization (WHO), Tedros Adhanom Ghebreyesus, announced that based on the assessment, WHO believes that the current outbreak of COVID-19 can be called a global pandemic. By early April 2020, there were more than one million confirmed cases worldwide. COVID-19 has developed from sporadic cases to pandemic in a short period of 3 months. The analysis and research of its infectious data will help to prevent and control the next stage of epidemic prevention and other infectious diseases in the future. In this paper, COVID-19 rounded average of seven days ( M ), and M’s ring ratio ( R ) are used to predict the current potential patients’ data, and the relative state of epidemic prevention and control is judged through the graphic features and characteristic data, so as to provide evidence for the prevention and control decisions.