A mathematical model for the spread of Omicron virus based on grey prediction algorithm

Abstract

After two years of the Newcastle epidemic, the emergence of a mutant strain of Omicron virus has once again raised a high level of alarm worldwide, and it has rapidly replaced the Delta strain as the major strain in the world epidemic with high infectivity. In order to study the spread of the epidemic in a qualitative and quantitative manner, this paper uses relevant mathematical modeling ideas and methods, combined with the background of big data, to conduct relevant research and analysis in order to provide guidance for the actual fight against the epidemic and facilitate scientific research and response measures. In this paper, a gray prediction algorithm is used to predict the future epidemic data. A metabolic algorithm is introduced to recursively correct the prediction residuals and build a multilevel gray model. In this paper, to investigate the role of vaccines on epidemics, the R values under each dose of vaccine were calculated by selecting data from several countries and regions using fitted curve idealization. The prediction of the data yields the conclusion that vaccination is indeed directly effective in controlling the spread of the epidemic.