Analysis and predication of tuberculosis registration rates in Henan Province, China: an exponential smoothing model study

Zhang, Yanqiu; Li, Xinxu; Li, Weibin; Jiang, Jian-Guo; Zhang, Guolong; Zhuang, Yan; Xu, Jiying; Shi, Jie; Sun, Dingyong

doi:10.1186/s40249-020-00742-y

Cited by 14 publications

(16 citation statements)

References 27 publications

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“…The traditional single seasonal ES models have a limited capacity to deal with such a complex time series composing of multiple seasonal behaviors, non-integer seasonal behaviors, and dual-calendar effects, 21 despite their widespread use in practice. 12 , 34 Some researchers strive for the extension of the single seasonal Holt-Winters’ method by accommodating a second seasonal pattern to analyze the time series including two seasonal behaviors. 20 However, this extended model was shown to suffer from over-parameterization as there are a large number of parameters that require to be calculated for the preliminary seasonal behaviors, especially when the target series is composed of high-frequency seasonal behaviors, further heightening the need for the modifications of the traditional ES approaches to deal with a wider variety of seasonal patterns and to resolve the issues raised above.…”

Section: Methodsmentioning

confidence: 99%

“…There are a large volume of published researches that developed different mathematical techniques to analyze and estimate the upcoming epidemics of infectious diseases for different forecasting aims. The common forecasting tools include autoregressive integrated moving average (ARIMA) model, 11 exponential smoothing (ES) methods, 12 generalized regression neural network method, 13 autoregressive distributed lag method, 14 grey approaches, 15 back-propagation neural network technique, 16 generalized linear regression models, 17 support vector machine regression method, 18 and autoregressive conditional heteroskedasticity (ARCH) method. 19 The above-mentioned methods may provide a satisfactory result in their respective prediction domain.…”

Section: Introductionmentioning

confidence: 99%

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

Xiao¹,

Li²,

Li³

et al. 2021

IDR

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Xiao¹,

Li²,

Li³

et al. 2021

IDR

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“…In the past, the well-documented seasonal ES methods have widely been adopted to deal with the single seasonal time series. 17 Nevertheless, these traditional ES methods fail to describe the complex seasonal time series comprising multiple seasonal patterns, non-integer seasonal patterns, and dualcalendar effects. 21,34 Although some researchers attempted to extend the classical ES models to accommodate a second seasonal trait so that the time series comprising two seasonal components can be described, this may suffer from a major flaw that needs to compute substantial values for the preceding seasonal patterns, particularly when a time series shows highfrequency seasonal behaviors, resulting in overparameterization.…”

Section: Tbats Modelmentioning

confidence: 99%

“…14,15 Therefore, to attenuate or contain the spreading of this illness, early warning for the temporal patterns of HFMD epidemic trends in the upcoming years using a suitable forecasting model plays an important role in developing effective preventive measures. 16 In the past, different mathematical simulation models, including autoregressive integrated moving average (ARIMA) method, 13 artificial neural networks (ANN S ), exponential smoothing (ES) method, 17 support vector machine (SVM), 18 decomposition methods, 18 and grey model 19 have been applied to forecast the epidemics of communicable diseases. The occurrence of communicable diseases is typically limited by different influencing factors (eg, meteorological variables, government interventions, vaccines, and air quality 15,20 ), leading to showing complex epidemiological features with multiple seasonal periods, high-frequency seasonality, non-integer seasonality, and dual-calendar effects of the time series.…”

Section: Introductionmentioning

confidence: 99%

Time Series Analysis and Forecasting of the Hand-Foot-Mouth Disease Morbidity in China Using An Advanced Exponential Smoothing State Space TBATS Model

Yu¹,

Xu²,

Li³

et al. 2021

IDR

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The high morbidity, complex seasonality, and recurring risk of hand-foot-andmouth 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, {<12,4>}) 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, {<12,4>}) 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.

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“…There is a large volume of studies forecasting the epidemiological trends of communicable diseases using different statistical techniques, such as the seasonal autoregressive integrated moving average (SARIMA) method, 6 exponential smoothing method, 7 generalized regression neural network (GRNN) method, 8 nonlinear autoregressive neural network (NARNN) method, 9 backpropagation neural network (BPNN) method, 1 multivariate linear regression method, 10 and Elman and Jordan recurrent neural networks. 11 Among them, the most frequently used linear method is the SARIMA model, [12][13][14][15] whereas the nonlinear method is the NARNN model.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Tuberculosis Incidence Using a Novel Ensemble Empirical Mode Decomposition-Based Data-Driven Hybrid Model in Tibet, China

Li²,

et al. 2021

IDR

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The purpose of this study is to develop a novel data-driven hybrid model by fusing ensemble empirical mode decomposition (EEMD), seasonal autoregressive integrated moving average (SARIMA), with nonlinear autoregressive artificial neural network (NARNN), called EEMD-ARIMA-NARNN model, to assess and forecast the epidemic patterns of TB in Tibet. Methods: The TB incidence from January 2006 to December 2017 was obtained, and then the time series was partitioned into training subsamples (from January 2006 to December 2016) and testing subsamples (from January to December 2017). Among them, the training set was used to develop the EEMD-SARIMA-NARNN combined model, whereas the testing set was used to validate the forecasting performance of the model. Whilst the forecasting accuracy level of this novel method was compared with the basic SARIMA model, basic NARNN model, error-trend-seasonal (ETS) model, and traditional SARIMA-NARNN mixture model. Results: By comparing the accuracy level of the forecasting measurements including rootmean-square error, mean absolute deviation, mean error rate, mean absolute percentage error, and root-mean-square percentage error, it was shown that the EEMD-SARIMA-NARNN combined method produced lower error rates than the others. The descriptive statistics suggested that TB was a seasonal disease, peaking in late winter and early spring and a trough in autumn and early winter, and the TB epidemic indicated a drastic increase by a factor of 1.7 from 2006 to 2017 in Tibet, with average annual percentage change of 5.8 (95% confidence intervals: 3.5-8.1). Conclusion: This novel data-driven hybrid method can better consider both linear and nonlinear components in the TB incidence than the others used in this study, which is of great help to estimate and forecast the future epidemic trends of TB in Tibet. Besides, under present trends, strict precautionary measures are required to reduce the spread of TB in Tibet.

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Analysis and predication of tuberculosis registration rates in Henan Province, China: an exponential smoothing model study

Cited by 14 publications

References 27 publications

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

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

Time Series Analysis and Forecasting of the Hand-Foot-Mouth Disease Morbidity in China Using An Advanced Exponential Smoothing State Space TBATS Model

Forecasting the Tuberculosis Incidence Using a Novel Ensemble Empirical Mode Decomposition-Based Data-Driven Hybrid Model in Tibet, China

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