Spatiotemporal dynamics, clustering, and ARIMA-based short-term prediction of human brucellosis in Xinjiang, China

Brucellosis, a global zoonotic disease caused by Brucella spp., poses a severe threat to public health and livestock husbandry in Xinjiang, China's major pastoral region. Despite control efforts, its recent resurgence and complex spatiotemporal dynamics remain inadequately characterized. This study aimed to analyze brucellosis' temporal trends, spatial clustering, and short-term forecasts in Xinjiang (2010-2023) to inform targeted interventions.

Prefecture-level incidence data (2010-2023) were collected, with ARIMA (2,1,1) time-series modeling for forecasting and global/local spatial autocorrelation analysis (Moran's I, LISA) to identify clustering.

A total of 68,437 cases were reported, showing three distinct phases: upward (2010-2015, peak: 8,820 cases), decline (2016-2020), and rebound (2021-2023). Cases exhibited stable seasonal peaks (May-August, 57.9%-62.1% of annual cases). The ARIMA model demonstrated high predictive accuracy (adjusted R²=0.89), with short-term projections of 10,021 cases in 2024 and 10,583 cases in 2025. Spatially, incidence shifted from negative autocorrelation (2010-2020) to weak positive autocorrelation (2021-2023), with hotspots moving from Northern/Eastern Xinjiang to western regions (e.g., Kizilesu Keerkezi Autonomous Prefecture) and persistent cold spots in southern Xinjiang.

This study elucidates the dynamic spatiotemporal patterns of brucellosis in Xinjiang, shaped by livestock practices, environmental factors, and interventions. The forecasts and clustering insights support region-specific One Health strategies, our findings provide an analytical framework and actionable evidence for precision brucellosis surveillance in pastoral zones across Central Asia.

Not applicable.