基于集合经验模态分解和自回归-移动平均模型的COVID-19流行病全球预测系统预测结果改进

2020年,新型冠状病毒肺炎(COVID-19)在世界范围内迅速传播.为准确预测各国每日新增发病人数,兰州大学开发了 COVID-19流行病全球预测系统(GPCP).在本文的研究中,我们使用集合经验模态分解(EEMD)模型和自回归-移动平均(ARMA)模型对GPCP的预测结果进行改进,并对发病人数较少或处于发病初期,不完全符合传染病规律,GPCP模型无法预测的国家进行直接预测.从结果来看,使用该方法修正预测结果,古巴等国家预测误差均大幅下降,且预测趋势更接近真实情况.对于萨尔瓦多等发病人数较少的国家直接进行预测,相对误差较小,预测结果较为准确.该方法对于改进预测结果和直接预测均较为有效.

Improvement of the global prediction system of the COVID-19 pandemic based on the ensemble empirical mode decomposition(EEMD)and autoregressive moving average(ARMA)model in a hybrid approach

In 2020,the COVID-19 pandemic spreads rapidly around the world.To accurately predict the number of daily new cases in each country,Lanzhou University has established the Global Prediction System of the COVID-19 Pandemic(GPCP).In this article,the authors use the ensemble empirical mode decomposition(EEMD)model and autoregressive moving average(ARMA)model to improve the prediction results of GPCP.In addition,the authors also conduct direct predictions for those countries with a small number of confirmed cases or are in the early stage of the disease,whose development trends of the pandemic do not fully comply with the law of infectious diseases and cannot be predicted by the GPCP model.Judging from the results,the absolute values of the relative errors of predictions in countries such as Cuba have been reduced significantly and their prediction trends are closer to the real situations through the method mentioned above to revise the prediction results out of GPCP.For countries such as El Salvador with a small number of cases,the absolute values of the relative errors of prediction become smaller.Therefore,this article concludes that this method is more effective for improving prediction results and direct prediction.