Forecasting the Number of New Coronavirus Infections Us-ing an Improved Grey Prediction Model

Hui Li; Bo Zeng; Jianzhou Wang; Hua’an Wu

doi:10.18502/ijph.v50i9.7057

Hui Li Chongqing Technology and Business University, Chongqing, China
Bo Zeng Chongqing Technology and Business University, Chongqing, China
Jianzhou Wang Chongqing Technology and Business University, Chongqing, China
Hua’an Wu Chongqing Technology and Business University, Chongqing, China

DOI: https://doi.org/10.18502/ijph.v50i9.7057

Keywords: New coronavirus; Forecasting the number of infections; Grey prediction model; Background value optimization; Particle swarm optimization

Abstract

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 for-ward specific suggestions.