Dataset Information

Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model.

ABSTRACT: OBJECTIVE:Tuberculosis (TB) remains a major deadly threat in mainland China. Early warning and advanced response systems play a central role in addressing such a wide-ranging threat. The purpose of this study is to establish a new hybrid model combining a seasonal autoregressive integrated moving average (SARIMA) model and a non-linear autoregressive neural network with exogenous input (NARNNX) model to understand the future epidemiological patterns of TB morbidity. METHODS:We develop a SARIMA-NARNNX hybrid model for forecasting future levels of TB incidence based on data containing 255 observations from January 1997 to March 2018 in mainland China, and the ultimate simulating and forecasting performances were compared with the basic SARIMA, non-linear autoregressive neural network (NARNN) and error-trend-seasonal (ETS) approaches, as well as the SARIMA-generalised regression neural network (GRNN) and SARIMA-NARNN hybrid techniques. RESULTS:In terms of the root mean square error, mean absolute error, mean error rate and mean absolute percentage error, the identified best-fitting SARIMA-NARNNX combined model with 17 hidden neurons and 4 feedback delays had smaller values in both in-sample simulating scheme and the out-of-sample forecasting scheme than the preferred single SARIMA(2,1,3)(0,1,1)12 model, a NARNN with 19 hidden neurons and 6 feedback delays and ETS(M,A,A), and the best-performing SARIMA-GRNN and SARIMA-NARNN models with 32 hidden neurons and 6 feedback delays. Every year, there was an obvious high-risk season for the notified TB cases in March and April. Importantly, the epidemic levels of TB from 2006 to 2017 trended slightly downward. According to the projection results from 2018 to 2025, TB incidence will continue to drop by 3.002% annually but will remain high. CONCLUSIONS:The new SARIMA-NARNNX combined model visibly outperforms the other methods. This hybrid model should be used for forecasting the long-term epidemic patterns of TB, and it may serve as a beneficial and effective tool for controlling this disease.

SUBMITTER: Wang Y

PROVIDER: S-EPMC6678063 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model.

Wang Yongbin Y Xu Chunjie C Zhang Shengkui S Wang Zhende Z Yang Li L Zhu Ying Y Yuan Juxiang J

BMJ open 20190731 7

<h4>Objective</h4>Tuberculosis (TB) remains a major deadly threat in mainland China. Early warning and advanced response systems play a central role in addressing such a wide-ranging threat. The purpose of this study is to establish a new hybrid model combining a seasonal autoregressive integrated moving average (SARIMA) model and a non-linear autoregressive neural network with exogenous input (NARNNX) model to understand the future epidemiological patterns of TB morbidity.<h4>Methods</h4>We dev ...[more]

PMID: 31371283

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Project description:BackgroundScarlet fever is recognized as being a major public health issue owing to its increase in notifications in mainland China, and an advanced response based on forecasting techniques is being adopted to tackle this. Here, we construct a new hybrid method incorporating seasonal autoregressive integrated moving average (SARIMA) with a nonlinear autoregressive with external input(NARX) to analyze its seasonality and trend in order to efficiently prevent and control this re-emerging disease.MethodsFour statistical models, including a basic SARIMA, basic nonlinear autoregressive (NAR) method, traditional SARIMA-NAR and new SARIMA-NARX hybrid approaches, were developed based on scarlet fever incidence data between January 2004 and July 2018 to evaluate its temporal patterns, and their mimic and predictive capacities were compared to discover the optimal using the mean absolute percentage error, root mean square error, mean error rate, and root mean square percentage error.ResultsThe four preferred models identified were comprised of the SARIMA(0,1,0)(0,1,1)12, NAR with 14 hidden neurons and five delays, SARIMA-NAR with 33 hidden neurons and five delays, and SARIMA-NARX with 16 hidden neurons and 4 delays. Among which presenting the lowest values of the aforementioned indices in both simulation and prediction horizons is the SARIMA-NARX method. Analyses from the data suggested that scarlet fever was a seasonal disease with predominant peaks of summer and winter and a substantial rising trend in the scarlet fever notifications was observed with an acceleration of 9.641% annually, particularly since 2011 with 12.869%, and moreover such a trend will be projected to continue in the coming year.ConclusionsThe SARIMA-NARX technique has the promising ability to better consider both linearity and non-linearity behind scarlet fever data than the others, which significantly facilitates its prevention and intervention of scarlet fever. Besides, under current trend of ongoing resurgence, specific strategies and countermeasures should be formulated to target scarlet fever.

Project description:BackgroundTuberculosis (TB) remained one of the world's most deadly chronic communicable diseases. Future TB incidence prediction is a benefit for intervention options and resource-allocation planning. We aimed to develop rapid univariate prediction models for epidemics forecasting employment.MethodsThe surveillance data regarding Taiwan monthly TB incidence rates which from January 2005 to June 2017 were utilized for simulation modelling and from July 2017 to December 2020 for model validation. The modeling approaches including the Seasonal Autoregressive Integrated Moving Average (SARIMA), the Exponential Smoothing (ETS), and SARIMA-ETS hybrid algorithms were constructed and compared. The modeling performance of in-sample simulating training sets and pseudo-out-of-sample validating sets were evaluated by metrics of the root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and mean absolute scaled error (MASE).ResultsA total of 191,526 TB cases with a highest incidence rate in 2005 (72.5 per 100,000 person-year) and lowest in 2020 (33.2 per 100,000 person-year), from January-2005 to December-2020 showed a seasonality and steadily declining trend in Taiwan. The monthly incidence rates data were utilized to formulate these forecasting models. Through stepwise screening and assessing of the accuracy metrics, the optimized SARIMA(3,0,0)(2,1,0)12, ETS(A,A,A) and SARIMA-ETS-hybrid models were respectively selected as the candidate models. Regarding the outcome assessment of model performance, the SARIMA-ETS-hybrid model outperformed the ARIMA and ETS in the short term prediction with metrics of RMSE, MAE MAPE, and MASE of 0.084%, 0.067%, 0.646%, and 0.870%, during the pseudo-out-of-sample forecasting period. After projecting ahead to the long term forecasting TB incidence rates, ETS model showed the best performance resulting as a 41.69% (range: 22.1-56.38%) reduction of TB epidemics in 2025 and a 54.48% (range: 33.7-68.7%) reduction in 2030 compared with the 2015 levels.ConclusionThis time series modeling might offer us a rapid surveillance tool for facilitating WHO's future TB elimination milestone. Our proposed SARIMA-ETS or ETS model outperformed the SARIMA in predicting less or 12-30 months ahead of epidemics, and all models showed better in short or medium-term forecasting than long-term forecasting.

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Project description:BackgroundHyperuricemia (HUA) is becoming a global public health problem and associated with multiple diseases.ObjectiveWe conducted a systematic review to synthesize the pooled prevalence of HUA in mainland China and delineate its demographic, regional, and temporal trends over the past two decades.MethodsSystematic literature searches of PubMed, SCOPUS, Web of Science, the China National Knowledge Infrastructure (CNKI), and the Wanfang digital database were conducted to screen studies published from 1 January 2000 to 31 August 2019, reporting the prevalence of HUA in mainland China. The search strings were ('hyperuricemia' OR 'hyperuricaemia' OR 'uric acid') AND ('prevalence' OR 'epidemiology') AND 'China'. Article quality was appraised quantitatively from 11 items. Before formal meta-analysis, age-standardized prevalence was transformed. The random-effects model was applied to synthesize the pooled prevalence due to its high heterogeneity. Then we stratified the prevalence estimates by age, gender, area, nationality, and publication year for subgroup analysis.ResultsTotally 177 eligible studies with a whole population of 2,277,712 were included in the present meta-analysis. The pooled prevalence in mainland China was estimated at 16.4% (95% CI: 15.3%~17.6%). In studies with the onset age at 20 ~ 29 years old, males had a twice times higher HUA prevalence than females (21.5% vs. 8.9%). The prevalence of HUA was 13.7% (11.8%~15.7%) in people aged 15~ years old, 16.5% in 30~ (14.8%~18.4%), 17.9% in 40~ (16.4%~19.5%), 19.4% in 50~ (17.8%~21.0%), 20.5% in 60~ (18.8%~22.3%), and 24.9% in over 70 (22.9%~27.1%). Stratified by regions, southern (25.5%) and southwestern (21.2%) China shared the highest prevalence, and the lowest prevalence was observed in the northwest (12.6%). From 2001 to 2017, the prevalence estimates of HUA steadily climbed from 8.5% to 18.4% with minor fluctuations. Multiple regression revealed that HUA prevalence was positively correlated to the larger sample size, area, advanced onset age, and published year.ConclusionsThe last two decades witnessed the rapid growth prevalence of HUA in China. Early screening and personalized health education for HUA need to be given enough attention.

Dataset Information

Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model.

Publications

Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model.

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