Li Hui, Zeng Bo, Wang Jianzhou, Wu Hua'an
Chongqing Technology and Business University, Chongqing, China.
Iran J Public Health. 2021 Sep;50(9):1842-1853. doi: 10.18502/ijph.v50i9.7057.
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.
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,), this paper uses particle swarm optimization algorithm to optimize the background value, and obtains a new improved grey prediction model called GM(1,1|,,).
Through MATLAB simulation, the comprehensive percentage error of GM(1,1|,,), NHGM(1,1,), 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.
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.
近期，一种新型冠状病毒正在中国武汉迅速传播。科学有效地预测感染人数对于医疗资源的分配和救援效率的提高具有重要意义。
新型冠状病毒感染人数在短期内呈现出“数据少、信息贫”的特点。灰色预测模型为研究“数据少、信息贫”的预测问题提供了一种有效方法。本文基于对NHGM(1,1)的序列优化，采用粒子群优化算法对背景值进行优化，得到一种新的改进灰色预测模型GM(1,1|,,)。
通过MATLAB仿真，GM(1,1|,,)、NHGM(1,1)、UGM(1,1)、DGM(1,1)的综合百分比误差分别为2.4440%、11.7372%、11.6882%和59.9265%，因此新模型具有最佳的预测性能。利用新模型对新型冠状病毒感染人数进行了预测。
未来两周中国新型冠状病毒感染人数持续增加，最终感染人数近10万。基于预测结果，本文提出了具体建议。
