Qi Bangguo, Liu Nankun, Yu Shicheng, Tan Feng
Chinese Center for Disease Control and Prevention, Beijing, China.
China CDC Wkly. 2022 Dec 30;4(52):1185-1188. doi: 10.46234/ccdcw2022.239.
To compare the performance between the compartment model and the autoregressive integrated moving average (ARIMA) model that were applied to the prediction of new infections during the coronavirus disease 2019 (COVID-19) epidemic.
The compartment model and the ARIMA model were established based on the daily cases of new infection reported in China from December 2, 2019 to April 8, 2020. The goodness of fit of the two models was compared using the coefficient of determination (R).
The compartment model predicts that the number of new cases without a cordon sanitaire, i.e., a restriction of mobility to prevent spread of disease, will increase exponentially over 10 days starting from January 23, 2020, while the ARIMA model shows a linear increase. The calculated R values of the two models without cordon sanitaire were 0.990 and 0.981. The prediction results of the ARIMA model after February 2, 2020 have a large deviation. The R values of complete transmission process fit of the epidemic for the 2 models were 0.964 and 0.933, respectively.
The two models fit well at different stages of the epidemic. The predictions of compartment model were more in line with highly contagious transmission characteristics of COVID-19. The accuracy of recent historical data had a large impact on the predictions of the ARIMA model as compared to those of the compartment model.
比较用于预测2019年冠状病毒病（COVID-19）疫情期间新感染病例的 compartment 模型与自回归积分移动平均（ARIMA）模型的性能。
基于2019年12月2日至2020年4月8日中国报告的每日新增感染病例数，建立 compartment 模型和 ARIMA 模型。使用决定系数（R）比较这两种模型的拟合优度。
compartment 模型预测，在没有实施封控措施（即限制人员流动以防止疾病传播）的情况下，从2020年1月23日开始的10天内，新增病例数将呈指数增长，而 ARIMA 模型显示为线性增长。两种未实施封控措施模型的计算R值分别为0.990和0.981。2020年2月2日之后，ARIMA 模型的预测结果偏差较大。两种模型对疫情完整传播过程拟合的R值分别为0.964和0.933。
两种模型在疫情的不同阶段拟合效果良好。compartment 模型的预测更符合 COVID-19 的高传染性传播特征。与 compartment 模型相比，近期历史数据的准确性对 ARIMA 模型的预测有较大影响。
