Background: Recently, a new coronavirus has been rapidly spreading from Wuhan, China. Forecasting the number of infections scientifically and effectively is of great significance to the allocation of medical resources and the improvement of rescue efficiency.
Methods: The number of new coronavirus infections was characterized by "small data, poor information" in the short term. The grey prediction model provides an effective method to study the prediction problem of "small data, poor information". Based on the order optimization of NHGM(1,1,k), this paper uses particle swarm optimization algorithm to optimize the background value, and obtains a new improved grey prediction model called GM(1,1|r,c,u).
Results: Through MATLAB simulation, the comprehensive percentage error of GM(1,1|r,c,u), NHGM(1,1,k), UGM(1,1), DGM(1,1) are 2.4440%, 11.7372%, 11.6882% and 59.9265% respectively, so the new model has the best prediction performance. The new coronavirus infections was predicted by the new model.
Conclusion: The number of new coronavirus infections in China increased continuously in the next two weeks, and the final infections was nearly 100 thousand. Based on the prediction results, this paper puts forward specific suggestions.
Keywords: Background value optimization; Forecasting the number of infections; Grey prediction model; New coronavirus; Particle swarm optimization.
Copyright © 2021 Li et al. Published by Tehran University of Medical Sciences.