ArticleEpidemiology and infection2018
Time-series analysis of tuberculosis from 2005 to 2017 in China.
Article in Epidemiology and infection, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Temporal trends in tuberculosis incidence among the aging population in Southwest China: a retrospective study.BMC geriatrics · 2026Article
- Time trend prediction of multidrug-resistant/rifampicin-resistant tuberculosis in treatment initiation centers of North East Ethiopia (2015-2023).BMC infectious diseases · 2025Article
- Spatiotemporal distribution and detection of spatial clusters of tuberculosis in Hubei Province, China using FleXScan (2017-2023).BMC public health · 2025Article
- Long-term impact of COVID-19-related nonpharmaceutical interventions on tuberculosis: an interrupted time series analysis using Bayesian method.Journal of global health · 2025Article
- Prediction analysis of human brucellosis cases in Ili Kazakh Autonomous Prefecture Xinjiang China based on time series.Scientific reports · 2025Article
- Trends and impact of government prevention policies on pulmonary tuberculosis notification and mortality in Ningbo, China (2004-2021).Frontiers in tuberculosis · 2025Article
- Forecasting the trend of tuberculosis incidence in Anhui Province based on machine learning optimization algorithm, 2013-2023.BMC pulmonary medicine · 2024Article
- Predicting the incidence of rifampicin resistant tuberculosis in Yunnan, China: a seasonal time series analysis based on routine surveillance data.BMC infectious diseases · 2024Article
- Prediction of global omicron pandemic using ARIMA, MLR, and Prophet models.Scientific reports · 2022Article
- 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
- Forecasting the monthly incidence of scarlet fever in Chongqing, China using the SARIMA model.Epidemiology and infection · 2022Article
- The association between diurnal temperature range and clinic visits for upper respiratory tract infection among college students in Wuhan, China.Environmental science and pollution research international · 2022Article
- Application of the ARIMA Model in Forecasting the Incidence of Tuberculosis in Anhui During COVID-19 Pandemic from 2021 to 2022.Infection and drug resistance · 2022Article
- Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania.PLoS neglected tropical diseases · 2021Article
- Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia.International journal of environmental research and public health · 2021Article
- The association between extreme temperature and pulmonary tuberculosis in Shandong Province, China, 2005-2016: a mixed method evaluation.BMC infectious diseases · 2021Article
- Forecasting the incidence of mumps in Chongqing based on a SARIMA model.BMC public health · 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 hand-foot-and-mouth disease cases using wavelet-based SARIMA-NNAR hybrid model.PloS one · 2021Article
- Impact of COVID-19 Pandemic on Pre-Treatment Delays, Detection, and Clinical Characteristics of Tuberculosis Patients in Ningxia Hui Autonomous Region, China.Frontiers in public health · 2021Article
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4 authors.
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Abstract
Seasonal autoregressive integrated moving average (SARIMA) has been used to model nationwide tuberculosis (TB) incidence in other countries. This study aimed to characterise monthly TB notification rate in China. Monthly TB notification rate from 2005 to 2017 was used. Time-series analysis was based on a SARIMA model and a hybrid model of SARIMA-generalised regression neural network (GRNN) model. A decreasing trend (3.17% per years, P < 0.01) and seasonal variation of TB notification rate were found from 2005 to 2016 in China, with a predominant peak in spring. A SARIMA model of ARIMA (0,1,1) (0,1,1)12 was identified. The mean error rate of the single SARIMA model and the SARIMA-GRNN combination model was 6.07% and 2.56%, and the determination coefficient was 0.73 and 0.94, respectively. The better performance of the SARIMA-GRNN combination model was further confirmed with the forecasting dataset (2017). TB is a seasonal disease in China, with a predominant peak in spring, and the trend of TB decreased by 3.17% per year. The SARIMA-GRNN model was more effective than the widely used SARIMA model at predicting TB incidence.
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