An intelligent prediction model of epidemic characters based on multi-feature

Abstract

The epidemic characters of Omicron (e.g. large-scale transmission) are significantly different from the initial variants of COVID-19. The data generated by large-scale transmission is important to predict the trend of epidemic characters. However, the results of current prediction models are inaccurate since they are not closely combined with the actual situation of Omicron transmission. In consequence, these inaccurate results have negative impacts on the process of the manufacturing and the service industry, for example, the production of masks and the recovery of the tourism industry. The authors have studied the epidemic characters in two ways, that is, investigation and prediction. First, a large amount of data is collected by utilising the Baidu index and conduct questionnaire survey concerning epidemic characters. Second, the β-SEIDR model is established, where the population is classified as Susceptible, Exposed, Infected, Dead and β-Recovered persons, to intelligently predict the epidemic characters of COVID-19. Note that β-Recovered persons denote that the Recovered persons may become Susceptible persons with probability β. The simulation results show that the model can accurately predict the epidemic characters.