ArticleBMJ open2019
Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model.
Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 58 citations in OpenAlex.
- Optimizing Tuberculosis Incidence Prediction: A Systematic Review of Hybrid Modeling Approaches and Machine Learning Techniques.Public health challenges · 2026Review
- Age-period-cohort analysis of pulmonary tuberculosis epidemiological trends from 2005 to 2024 and forecasts for 2035 in Sichuan Province, China.Frontiers in public health · 2026Article
- Impact of policy shifts and festival holidays on tuberculosis surveillance: a time-series analysis in Southwest China (2014-2024).Frontiers in public health · 2026Article
- Time series analysis and seasonality trends of SARS-CoV-2 in Ecuador (2020-2024): a four-year study.Frontiers in public health · 2025Article
- Analysis and forecasting of syphilis trends in mainland China based on hybrid time series models.Epidemiology and infection · 2024Article
- Evaluating the impact of COVID-19 outbreak on hepatitis B and forecasting the epidemiological trend in mainland China: a causal analysis.BMC public health · 2024Article
- Temporal disruption in tuberculosis incidence patterns during COVID-19: a time series analysis in China.PeerJ · 2024Article
- Tuberculosis incidence in Brazil: time series analysis between 2001 and 2021 and projection until 2030.Revista brasileira de epidemiologia = Brazilian journal of epidemiology · 2024Article
- Research of Combined ES-BP Model in Predicting Syphilis Incidence 1982-2020 in Mainland China.Iranian journal of public health · 2023Article
- Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China.BMC public health · 2023Article
- Bridging the Gap in End Tuberculosis Targets in the Elderly Population in Eastern China: Observational Study From 2015 to 2020.JMIR public health and surveillance · 2022Observational
- A multivariate multi-step LSTM forecasting model for tuberculosis incidence with model explanation in Liaoning Province, China.BMC infectious diseases · 2022Article
- Predictive analysis of the number of human brucellosis cases in Xinjiang, China.Scientific reports · 2021Article
- Forecasting the incidence of mumps in Chongqing based on a SARIMA model.BMC public health · 2021Article
- Use of meteorological parameters for forecasting scarlet fever morbidity in Tianjin, Northern China.Environmental science and pollution research international · 2021Article
- Predictive study of tuberculosis incidence by time series method and Elman neural network in Kashgar, China.BMJ open · 2021Article
- Forecasting the Tuberculosis Incidence Using a Novel Ensemble Empirical Mode Decomposition-Based Data-Driven Hybrid Model in Tibet, China.Infection and drug resistance · 2021Article
- Estimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis.Infection and drug resistance · 2021Article
- Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models.Frontiers in veterinary science · 2021Article
- Spatiotemporal characteristics and the epidemiology of tuberculosis in China from 2004 to 2017 by the nationwide surveillance system.BMC public health · 2020Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTuberculosis (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.
methodsWe 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.
resultsIn 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)
conclusionsThe 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.
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