ArticleBMC public health2025
Multiple change point analysis and time series forecast of brucellosis reports in Xinjiang, China from 2010 to 2024.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Forecasting monthly hepatitis B cases in China: a nationwide comparative study based on surveillance data.BMC public health · 2026Article
- Spatiotemporal dynamics, clustering, and ARIMA-based short-term prediction of human brucellosis in Xinjiang, China.BMC infectious diseases · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
objectiveBrucellosis represents a significant global challenge; however, epidemiological research on brucellosis in Xinjiang from a change point perspective has been lacking. This study aims to identify significant change points and trends, as well as forecast the number of brucellosis cases in Xinjiang, China, thereby offering recommendations for its prevention and control.
methodsChange points were identified through binary segmentation of the full dataset. The Autoregressive Integrated Moving Average (ARIMA) Model, Support Vector Regression (SVR), and ARIMA-SVR models were employed to forecast the number of reported brucellosis cases. Model performance was evaluated using RMSE, MAE, and MAPE, and the optimal model was selected to predict the monthly cases from 2025 to 2027.
resultsThe results showed five change points in the monthly brucellosis time series. The highest average number of reported brucellosis cases occurred between the fifth change point (January 2023) and the end of the series (December 2024). The ARIMA-SVR model outperformed both the ARIMA and SVR models in predicting brucellosis cases. It is noteworthy that the forecasted results indicate that brucellosis cases will remain at historically high levels over the next three years, with the peak months potentially shifting from June to May and July.
conclusionChange point analysis holds significant value in the field of epidemiology. The ARIMA-SVR model is suitable for predicting the incidence of brucellosis in Xinjiang, China. It is anticipated that the disease burden of brucellosis in Xinjiang will remain at a high level in the future, and local health authorities should continue to implement stringent targeted prevention and control measures. These research findings provide valuable insights for subsequent epidemiological studies and the development of a brucellosis early warning system.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.