ArticleScientific reports2025
Prediction analysis of human brucellosis cases in Ili Kazakh Autonomous Prefecture Xinjiang China based on time series.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Epidemiological trends and comparative forecasting models of human brucellosis in inner mongolia autonomous region, mainland China, 2004-2024.PLoS neglected tropical diseases · 2026Article
- Spatial characteristics and driving factors of human brucellosis and plague infections in theOne health (Amsterdam, Netherlands) · 2026Article
- Spatiotemporal Early Warning of Human Brucellosis in Hubei Province Using Coupled Ecological-Time Series Models.Zoonoses and public health · 2026Article
- Epidemiological characteristics and incidence prediction analysis of brucellosis in Bayingolin mongol autonomous prefecture, Xinjiang.BMC infectious diseases · 2026Article
- Desipramine treatment disrupts phagocytic and intracellular survival ofFrontiers in veterinary science · 2026Article
- Evaluation of non-vaccination brucellosis control strategies in class II regions using a transmission dynamics model: a case study from Hubei Province.BMC infectious diseases · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Human brucellosis remains a significant public health issue in the Ili Kazak Autonomous Prefecture, Xinjiang, China. To assist local Centers for Disease Control and Prevention (CDC) in promptly formulate effective prevention and control measures, this study leveraged time-series data on brucellosis cases from February 2010 to September 2023 in Ili Kazak Autonomous Prefecture. Three distinct predictive modeling techniques-Seasonal Autoregressive Integrated Moving Average (SARIMA), eXtreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks-were employed for long-term forecasting. Further, the optimal model will be used to explore the impact of COVID-19 on the transmission of Human brucellosis in the region. We constructed a SARIMA(4,1,1)(3,1,2)12 model, an XGBoost model with a time lag of 22, and an LSTM model featuring 3 LSTM layers and 100 neurons in the fully connected layer to predict monthly reported cases from January 2021 to September 2023. The results indicated that the occurrence of brucellosis exhibits pronounced seasonal patterns, with higher incidence during summer and autumn, peaking in June annually. Performance evaluations revealed low Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Symmetric Mean Absolute Percentage Error (SMAPE) for all three models. Specifically, the coefficient of determination (R
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