Estimating the Long-Term Epidemiological Trends and Seasonality of Hemorrhagic Fever with Renal Syndrome in China

Abstract

Objective We aim to examine the adequacy of an innovation state-space modeling framework (called TBATS) in forecasting the long-term epidemic seasonality and trends of hemorrhagic fever with renal syndrome (HFRS). Methods The HFRS morbidity data from January 1995 to December 2020 were taken, and subsequently, the data were split into six different training and testing segments (including 12, 24, 36, 60, 84, and 108 holdout monthly data) to investigate its predictive ability of the TBATS method, and its forecasting performance was compared with the seasonal autoregressive integrated moving average (SARIMA). Results The TBATS (0.27, {0,0}, -, {<12,4>}) and SARIMA (0,1,(1,3))(0,1,1) 12 were selected as the best TBATS and SARIMA methods, respectively, for the 12-step ahead prediction. The mean absolute deviation, root mean square error, mean absolute percentage error, mean error rate, and root mean square percentage error were 91.799, 14.772, 123.653, 0.129, and 0.193, respectively, for the preferred TBATS method and were 144.734, 25.049, 161.671, 0.203, and 0.296, respectively, for the preferred SARIMA method. Likewise, for the 24-, 36-, 60-, 84-, and 108-step ahead predictions, the preferred TBATS methods produced smaller forecasting errors over the best SARIMA methods. Further validations also suggested that the TBATS model outperformed the Error-Trend-Seasonal framework, with little exception. HFRS had dual seasonal behaviors, peaking in May–June and November–December. Overall a notable decrease in the HFRS morbidity was seen during the study period (average annual percentage change=−6.767, 95% confidence intervals: −10.592 to −2.778), and yet different stages had different variation trends. Besides, the TBATS model predicted a plateau in the HFRS morbidity in the next ten years. Conclusion The TBATS approach outperforms the SARIMA approach in estimating the long-term epidemic seasonality and trends of HFRS, which is capable of being deemed as a promising alternative to help stakeholders to inform future preventive policy or practical solutions to tackle the evolving scenarios.

Time series models show comparable projection performance with joinpoint regression: A comparison using historical cancer data from World Health Organization

“…Besides, joinpoint regression analysis and AAPC were also used to calculate the temporal trend and ARIMA model was applied to the 10-year projection of aortic aneurysm (45). Seasonal ARIMA (SARIMA) was also approved to show a strong ability in estimating the long-term epidemic treads of hemorrhagic fever with renal syndrome (HFRS) (46). Only aging-related cancers were included in this study.…”

The research on TBATS and ELM models for prediction of human brucellosis cases in mainland China: a time series study

“…Traditional seasonal exponentially smooth models are limited in their ability to handle multi-seasonal, non-integer seasonal, and dual-calendar time series [ 27 ]. In this context, several researchers have studied the problem of processing complex seasonal time series, and the traditional exponentially smooth model was modified to resolve this problem [ 27 ].…”

“…The BATS model was introduced to address complex time series, such as multi-seasonal, non-integer seasonal, and dual-calendar time series [ 28 ]. The basic model form is expressed as BATS(p, q, m1, m2,..., mT), where B is the Box-Cox transformation, A is the ARMA error, and T and S are the trend and seasonal components of the time series, respectively [ 27 ]. In addition, parameters p and q of the BATS model are the ARIMA model orders p and q, and m1, m2,…, and mT are the seasonal periods of the ARIMA model.…”

“…In 1944, Robert G. Brown proposed the exponential smoothing (ES) model, which is one of the most typical time series forecasting models for infectious diseases [ 26 ]. The TBATS model is a modification of the traditional ES model, and uses an exponential smoothing approach to solve complex periodic time series problems [ 27 ]. As a result, the TBATS model has the advantage of processing a wider variety of complex seasonal pattern time series [ 27 ] than the ES and ARIMA models.…”

“…The TBATS model is a modification of the traditional ES model, and uses an exponential smoothing approach to solve complex periodic time series problems [ 27 ]. As a result, the TBATS model has the advantage of processing a wider variety of complex seasonal pattern time series [ 27 ] than the ES and ARIMA models. The structural components of the TBATS model mainly consist of trigonometric seasonality, Box-Cox transformation, ARMA errors, and trend and seasonal components [ 28 ].…”