Infection and Drug Resistance (Jul 2021)
Time Series Analysis and Forecasting of the Hand-Foot-Mouth Disease Morbidity in China Using An Advanced Exponential Smoothing State Space TBATS Model
Abstract
Chongchong Yu,1,* Chunjie Xu,2,* Yuhong Li,3 Sanqiao Yao,1 Yichun Bai,1 Jizhen Li,1 Lei Wang,4 Weidong Wu,1 Yongbin Wang1 1Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People’s Republic of China; 2Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, People’s Republic of China; 3National Center for Tuberculosis Control and Prevention, China Center for Disease Control and Prevention, Beijing, People’s Republic of China; 4Center for Musculoskeletal Surgery, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt–Universität Zu Berlin and Berlin Institute of Health, Berlin, Germany*These authors contributed equally to this workCorrespondence: Yongbin WangDepartment of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, 453000, Henan Province, People’s Republic of ChinaEmail [email protected]: The high morbidity, complex seasonality, and recurring risk of hand-foot-and-mouth disease (HFMD) exert a major burden in China. Forecasting its epidemic trends is greatly instrumental in informing vaccine and targeted interventions. This study sets out to investigate the usefulness of an advanced exponential smoothing state space framework by combining Box-Cox transformations, Fourier representations with time-varying coefficients and autoregressive moving average (ARMA) error correction (TBATS) method to assess the temporal trends of HFMD in China.Methods: Data from January 2009 to December 2019 were drawn, and then they were split into two segments comprising the in-sample training data and out-of-sample testing data to develop and validate the TBATS model, and its fitting and forecasting abilities were compared with the most frequently used seasonal autoregressive integrated moving average (SARIMA) method.Results: Following the modelling procedures of the SARIMA and TBATS methods, the SARIMA (1,0,1)(0,1,1)12 and TBATS (0.024, {1,1}, 0.855, {}) specifications were recognized as being the optimal models, respectively, for the 12-step ahead forecasting, along with the SARIMA (1,0,1)(0,1,1)12 and TBATS (0.062, {1,3}, 0.86, {}) models as being the optimal models, respectively, for the 24-step ahead forecasting. Among them, the optimal TBATS models produced lower error rates in both 12-step and 24-step ahead forecasting aspects compared to the preferred SARIMA models. Descriptive analysis of the data showed a significantly high level and a marked dual seasonal pattern in the HFMD morbidity.Conclusion: The TBATS model has the capacity to outperform the most frequently used SARIMA model in forecasting the HFMD incidence in China, and it can be recommended as a flexible and useful tool in the decision-making process of HFMD prevention and control in China.Keywords: HFMD, morbidity, TBATS, SARIMA, models, time series analysis
