摘要
目的根据时变易感者-潜伏者-感染者-隔离者-死亡者-康复者(susceptible-exposed-infected-quarantined-dead-removed,SEIQDR)模型和差分自回归移动平均(autoregressive integrated moving average,ARIMA)模型,针对上海市Omicron感染数据选择适合上海市疫情判断的预测模型。方法选用2022年3月1日―4月20日上海市COVID-19新增阳性感染者的数据进行拟合,选用2022年4月21日―5月30日的数据评估模型的预测效果。分别构建时变SEIQDR模型与ARIMA模型,通过比较决定系数(coefficient of determination,R^(2))、平均绝对误差(mean absolute error,MAE)和均方根误差(root mean squared error,RMSE)的大小评价模型的拟合及预测效果。结果时变SEIQDR模型和ARIMA模型的拟合效果均较优,R2分别为0.990和0.984。2个模型5 d的预测效果均尚可,对于20 d以及40 d预测效果,时变SEIQDR模型更优且更符合传染病传播的规律;前者的40 d预测MAE和RMSE分别为1001.461和1967.704,后者分别为1265.331和2068.094,且时变SEIQDR模型能较好地实现对上海市本轮疫情变化趋势及发病人数的复现。结论时变SEIQDR模型可较好地拟合及预测上海市COVID-19的发病人数及变化趋势,且模型效果优于ARIMA(2,2,0)。
Objective Based on the time-varying susceptible-exposed-infected-quarantined-dead-removed(SEIQDR)model and autoregressive integrated moving average(ARIMA)model,the Omicron infection data in Shanghai were studied and judged.The two models′fitting,prediction effect and applicability were analyzed and compared.And the prediction model with a better effect and suitable for developing the epidemic in Shanghai,China was selected.Methods Data from March 1,2022 to April 20,2022,for newly infected cases in Shanghai were selected for fitting.Data from April 21,2022 to May 30,2022,were used to evaluate the prediction effect of the model.Time-varying SEIQDR models and ARIMA models were constructed,respectively.The models′fitting and prediction effects were evaluated by comparing the magnitudes of R2,MAE,and RMSE.Results Both the time-varying SEIQDR model and the ARIMA model had a better fitting effect,with R^(2) of 0.990 and 0.984,respectively.The 5-day predictions of both models were fair,and the time-varying SEIQDR model was better and more consistent with the pattern of infectious disease transmission for the 20-day and 40-day predictions,with the 40-day predicted MAE and RMSE of 1001.461 and 1967.704,respectively,for the former and 1265.331 and 2068.094,respectively,for the latter.The time-varying SEIQDR model was able to achieve a more complete replication of the trend of the current epidemic and the number of incidences in Shanghai.Conclusions The time-varying SEIQDR model can better fit and predict the number and trend of COVID-19 in Shanghai,and the model effect is better than that of ARIMA(2,2,0).
作者
许书君
马艺菲
罗雨欣
郭嘉铭
王彤
李建涛
雷立健
贺鹭
余红梅
解军
XU Shujun;MA Yifei;LUO Yuxin;GUO Jiaming;WANG Tong;LI Jiantao;LEI Lijian;HE Lu;YU Hongmei;XIE Jun(Department of Health Statistics,School of Public Health,Shanxi Medical University,Taiyuan 030001,China;Department of Health Economics,School of Management,Shanxi Medical University,Taiyuan 030001,China;Department of Epidemiology,School of Public Health,Shanxi Medical University,Taiyuan 030001,China;Department of Social Medicine,School of Public Health,Shanxi Medical University,Taiyuan 030001,China;Center of Reverse Microbial Etiology,Shanxi Medical University,Taiyuan 030001,China)
出处
《中华疾病控制杂志》
CAS
CSCD
北大核心
2023年第11期1274-1281,1335,共9页
Chinese Journal of Disease Control & Prevention
基金
国家重点研发计划(2021YFC2301603)
山西省科技重大专项项目(202102130501003,202005D121008)。
