ArticleJournal of infection and public health
Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model.
Article in Journal of infection and public health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed.
- Prediction, lag and mixture effects of meteorology and pollutants on the incidence of pulmonary tuberculosis in Jining City, China.BMC public health · 2026Article
- Prediction of pulmonary tuberculosis case trends among older adults in Chongqing based on time series models.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
- Forecasting Foodborne Disease Risk Caused byFoods (Basel, Switzerland) · 2025Article
- A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict the epidemic trends of scrub typhus in China.PloS one · 2025Article
- A hybrid machine learning model for pulmonary tuberculosis forecasting of Chongqing with adjacent-region data.PloS one · 2025Article
- Incidence of acute hemorrhagic conjunctivitis in Chongqing: a forecasting study based on mathematical models.Frontiers in public health · 2025Article
- Evaluating the effectiveness of self-attention mechanism in tuberculosis time series forecasting.BMC infectious diseases · 2024Article
- Spatial and temporal analysis and forecasting of TB reported incidence in western China.BMC public health · 2024Article
- Forecasting and analyzing influenza activity in Hebei Province, China, using a CNN-LSTM hybrid model.BMC public health · 2024Article
- Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models.BMC public health · 2024Article
- A novel seasonal grey prediction model with fractional order accumulation for energy forecasting.Heliyon · 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
- Comparison of SARIMA model, Holt-winters model and ETS model in predicting the incidence of foodborne disease.BMC infectious diseases · 2023Article
- Analytical reference framework to analyze non-COVID-19 events.Population health metrics · 2023Article
- Article
- Time series analyses based on the joint lagged effect analysis of pollution and meteorological factors of hemorrhagic fever with renal syndrome and the construction of prediction model.PLoS neglected tropical diseases · 2023Article
- Article
- Time series analysis and forecasting of the number of canine rabies confirmed cases in Thailand based on national-level surveillance data.Frontiers in veterinary science · 2023Article
- Trends and Patterns of Systemic Glucocorticoid Prescription in Primary Care Institutions in Southwest China, from 2018 to 2021.Risk management and healthcare policy · 2023Article
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe aims of this study were to develop a forecasting model for the incidence of tuberculosis (TB) and analyze the seasonality of infections in China; and to provide a useful tool for formulating intervention programs and allocating medical resources.
methodsData for the monthly incidence of TB from January 2004 to December 2015 were obtained from the National Scientific Data Sharing Platform for Population and Health (China). The Box-Jenkins method was applied to fit a seasonal auto-regressive integrated moving average (SARIMA) model to forecast the incidence of TB over the subsequent six months.
resultsDuring the study period of 144 months, 12,321,559 TB cases were reported in China, with an average monthly incidence of 6.4426 per 100,000 of the population. The monthly incidence of TB showed a clear 12-month cycle, and a seasonality with two peaks occurring in January and March and a trough in December. The best-fit model was SARIMA (1,0,0)(0,1,1)
conclusionsAccording to the seasonal pattern of TB incidence in China, the SARIMA model was proposed as a useful tool for monitoring epidemics.
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