Trend Analysis and Intervention Effect Starting Point Detection of COVID-19 Epidemics Using Recalibrated Time Series Models—Worldwide,2020

作     者：Shu Li Chen Chen Shuyin Cao Kejia Hu Hao Lei Xiaolin Xu Qinchuan Wang Changzheng Yuan Sicong Wang Sisi Wang Junlin Jia Yuanqing Ye Xifeng Wu 

作者机构：Center of Clinical Big Data and AnalyticsSecond Affiliated Hospital and Department of Big Data Health Science School of Public HealthZhejiang University School of MedicineHangzhouZhejiangChina National Institute for Data Science in Health and MedicineHangzhouZhejiang UniversityZhejiangChina Department of Surgical OncologyAffiliated Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouZhejiangChina. 

出 版 物：《China CDC weekly》 (中国疾病预防控制中心周报（英文）)

年 卷 期：2021年第3卷第20期

页      面：417-422页

核心收录：

学科分类：1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1002[医学-临床医学] 100401[医学-流行病与卫生统计学] 10[医学] 

基　　金：Zhejiang University special scientific research fund for COVID-19 prevention and control(2020XGZX003) Zhejiang Provincial Innovation Team(2019R01007) Zhejiang Province Key Laboratory(2020E10004) Zhejiang Provincial Natural Science Foundation(LEZ20H260002). 

主　　题：starting absolute smoothing 

摘      要：Objective:This study aimed to identify a model for short-term coronavirus disease 2019(COVID-19)trend prediction and intervention evaluation.Methods:We compared the autoregressive integrated moving average(ARIMA)model and Holt exponential smoothing(Holt)model on predicting the number of cumulative COVID-19 cases in China.Based on the mean absolute percentage error(MAPE)value,the optimal model was selected and further tested using data from the United States,Italy and Republic of Korea.The intervention effect starting time points and abnormal trend changes were detected by observing the pattern of differences between the predicted and real trends.Results:The recalibrated ARIMA model with a 5-day prediction time span has the best model performance with MAPEs ranged between 2%and 5%.The intervention effects started to show on February 7 in the mainland of China,March 5 in Republic of Korea and April 27 in Italy,but have not been detected in the US as of May 19.Temporary abnormal trends were detected in Korea and Italy,but the overall epidemic trends were stable since the effect starting points.Conclusion:The recalibrated ARIMA model can detect the intervention effects starting points and abnormal trend changes;thus to provide valuable information support for epidemic trend analysis and intervention evaluation.