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

Yu, Chongchong; Xu, Chunjie; Li, Yuhong; Yao, Sanqiao; Bai, Yichun; Li, Jizhen; Wang, Lei; Wu, Weidong; Wang, Yongbin

doi:10.2147/idr.s304652

Cited by 16 publications

(21 citation statements)

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“…The time-series models were considered in this study for their capability to manage seasonality time-series data due to seasonal FMD outbreaks in Thailand [ 16 ]. Accordingly, (i) seasonal autoregressive integrated moving average (SARIMA) [ 29 ], (ii) error trend and seasonality (ETS) [ 30 ], and (iii) trigonometric exponential smoothing state–space model with Box–Cox transformation and an autoregressive moving average error, trend, and seasonality (TBATS) [ 30 , 31 ] methods were used together in the present study. Moreover, the application of neural network nonlinear autoregression (NNAR), a widely used machine learning method for nonlinear time-series data, was explored because FMD outbreak data may contain nonlinear patterns.…”

Section: Introductionmentioning

confidence: 99%

“…The main difference between NNAR and SARIMA models is that NNAR can manage nonlinear sequences of time-series data better than ARIMA [ 33 ], whereas SARIMA has a better capability to take account of a linear pattern in data than NNAR. Furthermore, it is possible to model time series with different seasonality using TBATS [ 30 , 31 ]. With this method, a Fourier series-based trigonometric representation is used to model seasonality.…”

Section: Introductionmentioning

confidence: 99%

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Time-Series Analysis for the Number of Foot and Mouth Disease Outbreak Episodes in Cattle Farms in Thailand Using Data from 2010–2020

Punyapornwithaya

Mishra

Sansamur

et al. 2022

Viruses

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Thailand is one of the countries where foot and mouth disease outbreaks have resulted in considerable economic losses. Forecasting is an important warning technique that can allow authorities to establish an FMD surveillance and control program. This study aimed to model and forecast the monthly number of FMD outbreak episodes (n-FMD episodes) in Thailand using the time-series methods, including seasonal autoregressive integrated moving average (SARIMA), error trend seasonality (ETS), neural network autoregression (NNAR), and Trigonometric Exponential smoothing state–space model with Box–Cox transformation, ARMA errors, Trend and Seasonal components (TBATS), and hybrid methods. These methods were applied to monthly n-FMD episodes (n = 1209) from January 2010 to December 2020. Results showed that the n-FMD episodes had a stable trend from 2010 to 2020, but they appeared to increase from 2014 to 2020. The outbreak episodes followed a seasonal pattern, with a predominant peak occurring from September to November annually. The single-technique methods yielded the best-fitting time-series models, including SARIMA(1,0,1)(0,1,1)12, NNAR(3,1,2)12,ETS(A,N,A), and TBATS(1,{0,0},0.8,{<12,5>}. Moreover, SARIMA-NNAR and NNAR-TBATS were the hybrid models that performed the best on the validation datasets. The models that incorporate seasonality and a non-linear trend performed better than others. The forecasts highlighted the rising trend of n-FMD episodes in Thailand, which shares borders with several FMD endemic countries in which cross-border trading of cattle is found common. Thus, control strategies and effective measures to prevent FMD outbreaks should be strengthened not only in Thailand but also in neighboring countries.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Time-Series Analysis for the Number of Foot and Mouth Disease Outbreak Episodes in Cattle Farms in Thailand Using Data from 2010–2020

Punyapornwithaya

Mishra

Sansamur

et al. 2022

Viruses

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“…However, as evidenced by the findings from our study and others, the SARIMA method is only appropriate for analyzing short- or medium-term trends and cannot deal with the nonlinear information in a complex time series due to its linear assumption. 13 , 16 , 52 , 53 Instead, the advanced TBATS method was developed by adding the trigonometric representation of seasonal components based on Fourier series to the traditional BATS approach, which allows it to not only deal with all of the complex time series but also to handle both linear and non-linear components, 23 , 25 whilst accommodating a dynamic seasonal patterns over time, 25 and hence leading to its usefulness and flexibility in analyzing the long-term trends and seasonality of time series. Given its attractive applications of the TBATS method and our previous work, 53 it appears that the importance of this advanced method as a powerful forecasting tool is expected to be emphasized in analyzing the long-term temporal levels of HFRS in other regions or other communicable diseases, and yet validation needs to be done.…”

Section: Discussionmentioning

confidence: 99%

“… 13 , 16 , 52 , 53 Instead, the advanced TBATS method was developed by adding the trigonometric representation of seasonal components based on Fourier series to the traditional BATS approach, which allows it to not only deal with all of the complex time series but also to handle both linear and non-linear components, 23 , 25 whilst accommodating a dynamic seasonal patterns over time, 25 and hence leading to its usefulness and flexibility in analyzing the long-term trends and seasonality of time series. Given its attractive applications of the TBATS method and our previous work, 53 it appears that the importance of this advanced method as a powerful forecasting tool is expected to be emphasized in analyzing the long-term temporal levels of HFRS in other regions or other communicable diseases, and yet validation needs to be done. Besides, some new advanced statistical techniques (eg, bayesian structural time series technique, 37 flexible transmitter network, 54 the optimized theta method, 55 and age-structure mathematical model 56 ) are also recently demonstrated to have the potential to make a long-term forecast for the time series.…”

Section: Discussionmentioning

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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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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“…A TBATS model [ 50 ] can accommodate complex seasonal behaviors of data [ 51 ] and is written as: TBATS(ω, p , q , φ , {m 1 , k 1 }, {m 2 , k 2 }, …, {m T , k T }), where ω is the Box-Cox transformation, k is the number of harmonics used for the seasonal trait, and φ is the dampening parameter.…”

Section: Methodsmentioning

confidence: 99%

A Novel Approach to Modeling and Forecasting Cancer Incidence and Mortality Rates through Web Queries and Automated Forecasting Algorithms: Evidence from Romania

Tudor

2022

Biology

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Cancer remains a leading cause of worldwide mortality and is a growing, multifaceted global burden. As a result, cancer prevention and cancer mortality reduction are counted among the most pressing public health issues of the twenty-first century. In turn, accurate projections of cancer incidence and mortality rates are paramount for robust policymaking, aimed at creating efficient and inclusive public health systems and also for establishing a baseline to assess the impact of newly introduced public health measures. Within the European Union (EU), Romania consistently reports higher mortality from all types of cancer than the EU average, caused by an inefficient and underfinanced public health system and lower economic development that in turn have created the phenomenon of “oncotourism”. This paper aims to develop novel cancer incidence/cancer mortality models based on historical links between incidence and mortality occurrence as reflected in official statistics and population web-search habits. Subsequently, it employs estimates of the web query index to produce forecasts of cancer incidence and mortality rates in Romania. Various statistical and machine-learning models—the autoregressive integrated moving average model (ARIMA), the Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend, and Seasonal Components (TBATS), and a feed-forward neural network nonlinear autoregression model, or NNAR—are estimated through automated algorithms to assess in-sample fit and out-of-sample forecasting accuracy for web-query volume data. Forecasts are produced with the overperforming model in the out-of-sample context (i.e., NNAR) and fed into the novel incidence/ mortality models. Results indicate a continuation of the increasing trends in cancer incidence and mortality in Romania by 2026, with projected levels for the age-standardized total cancer incidence of 313.8 and the age-standardized mortality rate of 233.8 representing an increase of 2%, and, respectively, 3% relative to the 2019 levels. Research findings thus indicate that, under the no-change hypothesis, cancer will remain a significant burden in Romania and highlight the need and urgency to improve the status quo in the Romanian public health system.

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

Cited by 16 publications

References 50 publications

Time-Series Analysis for the Number of Foot and Mouth Disease Outbreak Episodes in Cattle Farms in Thailand Using Data from 2010–2020

Time-Series Analysis for the Number of Foot and Mouth Disease Outbreak Episodes in Cattle Farms in Thailand Using Data from 2010–2020

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

A Novel Approach to Modeling and Forecasting Cancer Incidence and Mortality Rates through Web Queries and Automated Forecasting Algorithms: Evidence from Romania

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