A novel grey model based on traditional Richards model and its application in COVID-19. (January 2021)

• Record Type: Journal Article
• Authors: Luo, Xilin; Duan, Huiming; Xu, Kai
• Is Part Of: Chaos, solitons and fractals. Volume 142(2021)
• Publication Date: 2021-01
• Subjects: Grey prediction model -- COVID-19 -- Traditional Richards model -- Genetic algorithm optimization -- GERM(1, 1, eat); Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2020.110480
• Languages: English
• ISSNs: 0960-0779

Abstract

Highlights: A novel grey Richards model GERM(1, 1, e a t ) is proposed. The optimal nonlinear terms and background value of the novel model are determined by Genetic algorithm. The comparative study shows that the new model is superior to the other seven benchmark models. The predict the daily number of new confirmed cases of COVID-19 of four regions are projected. Abstract: In 2020, a new type of coronavirus is in the global pandemic. Now, the number of infected patients is increasing. The trend of the epidemic has attracted global attention. Based on the traditional Richards model and the differential information principle in grey prediction model, this paper uses the modified grey action quantity to propose a new grey prediction model for infectious diseases. This model weakens the dependence of the Richards model on single-peak and saturated S-shaped data, making Richards model more applicable, and uses genetic algorithm to optimize the nonlinear terms and the background value. To illustrate the effectiveness of the model, groups of slowly growing small-sample and large-sample data are selected for simulation experiments. Results of eight evaluation indexes show that the new model is better than the traditional GM(1, 1) and grey Richards model. Finally, this model is applied to China, Italy, Britain and Russia. The results show that the new model is better than the other 7 models. Therefore, this model can effectively predict the number of daily new confirmed cases of COVID-19, and provide important prediction information for the formulation of epidemic prevention policies.