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

Abstract

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.

A hybrid forecasting technique for infection and death from the mpox virus

“…They concluded that by using the appropriate methods that are suitable to the data, one can achieve consistent results in modelling and predicting any series of an epidemic. For more epidemic hybrid time series forecasting, see, for example, 34 – 42 .…”

Estimating the COVID-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition

“…It can be said that this data-driven mixture technique shows a strong capacity to improve the forecasting power for the prevalence and mortality data of COVID-19 in that the principal advantage of such a model facilitates to identify the preferred hybridization by decomposing the target data into various multi-scale levels to consider the underlying trend and random parts simultaneously by use of the different types of models. Given the forecasting superiority of our proposed data-driven hybrid method, it seems that this hybrid model is also useful in nowcasting and forecasting the epidemiological trends of the COVID-19 prevalence and mortality time series in other regions or other infectious diseases 44 . Of note, current studies found that some other forecasting tools (e.g., the new innovations state space modeling framework 59 , long short-term memory neural network 60 , advanced error-trend-seasonal (ETS) framework 61 , α-Sutte Indicator 62 , and SBDiEM 30 ) performed a highly accurate forecast for the epidemiological trends of COVID-19.…”

“…However, the above-referenced models are only a simple ensemble architecture comprising either a basic linear or nonlinear model based on the EEMD technique, which is unable to consider both linear and nonlinear components in a time series simultaneously despite a performance improvement over the basic models by use of these ensemble architectures. Motivated by the “decomposition and ensemble” idea based on the EEMD method, a promising alternative is to develop an ensemble architecture by integrating the linear trait with the nonlinear trait decomposed by the EEMD method using an adequate linear model and nonlinear model 44 . By doing so, this new ensemble architecture is capable of capturing both components in a time series simultaneously.…”

“…Second, the IMFs terms were modeled using appropriate NARANN methods, whereas the residue term was modeled with a suitable ARIMA model. Finally, the prediction results from our proposed hybrid model were obtained by a conjunction of those from the basic NARANN and ARIMA models 44 . Since the lack of adequate health infrastructure and services in many regions of Africa, such estimates can elucidate the spreading dynamics of the outbreak, which will be a useful aid for government institutions and policymakers to plan the number of additional materials and resources in order to keep the outbreak under control well.…”

Estimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis

“…2 Our estimated reductions in the TB morbidity are becoming slow, this is aligned with some earlier reports, 1,2,33 which also signals a slow falling or a recurring risk in the TB morbidity in some countries in recent years because of climate change, large-scale population migration, increasing drug-resistant TB, under-nutrition, co-infection with HIV, alcohol use disorders, smoking, together with the comorbid conditions of diabetes and hypertension. 2,43 Accordingly, to ensure that China is on track to reach the WHO's End TB Strategy, some additional technological breakthroughs that can substantially reduce the risk of developing TB among the susceptible population and comprehensive interventions (eg, optimization of the current prevention measures and the development of an effective post-exposure vaccine or a short, efficacious and safe treatment for TB infection 2,43 ) are required.…”