Fig 1.
Distribution Pattern of Human Brucellosis Incidence in the Chinese Southwest, 2004-2024.
Note: The figure illustrates the spatial distribution and temporal evolution of human brucellosis incidence in the Chinese Southwest (Tibet, Yunnan, Sichuan, Guizhou) from 2004 to 2024. Color intensity represents incidence levels, showing the transition from sporadic to widespread transmission over the study period. Data source: National Notifiable Disease Reporting System. Note: Map generation was completed in R. Chinese administrative boundary shapefiles originate from Tianditu National Geospatial Information Common Service Platform (https://cloudcenter.tianditu.gov.cn/administrativeDivision), official map review number GS (2024) 0650.
Fig 2.
Joinpoint Regression Analysis of Human Brucellosis Incidence in the Chinese Southwest, 2004-2024.
Note: Joinpoint regression analysis of human brucellosis incidence trends in the Chinese Southwest ((2A: Chongqing, Tibet, Yunnan) (2B: Guizhou, Sichuan,)) from 2004 to 2024. The analysis identifies significant turning points in incidence trends and calculates annual percentage changes (APC) for each segment. Notable regional variations are observed, with some areas showing significant increases (e.g., Dali (Yunnan Province) AAPC = 138.12*) while Nagqu City (Tibet) demonstrate declining trends (AAPC= -2.87). The plum symbolin the right plot indicate statistically significant joinpoints (p < 0.05) where trend directions changed substantially.
Fig 3.
Local Spatial Autocorrelation Analysis of Human Brucellosis Incidence in the Chinese Southwest, 2004-2024.
Note: Results of local spatial autocorrelation analysis (LISA) showing the spatial clustering patterns of human brucellosis incidence in the Chinese Southwest from 2004 to 2024. The map identifies: (1) High-High clusters (hot spots) indicating areas with significantly high incidence surrounded by similarly high areas; (2) Low-Low clusters (cold spots) showing areas with low incidence surrounded by low values; (3) spatial outliers including High-Low and Low-High areas. The analysis reveals significant geographical heterogeneity in disease distribution, with persistent hot spots in central Yunnan and western Sichuan, and cold spots in eastern Guizhou. All clusters shown are statistically significant (p < 0.05). Note: Map generation was completed in R. Chinese administrative boundary shapefiles originate from Tianditu National Geospatial Information Common Service Platform (https://cloudcenter.tianditu.gov.cn/administrativeDivision), official map review number GS (2024) 0650.
Table 1.
Serological dynamics of human brucellosis in the Chinese Southwest, 2006–2024.
Table 2.
Serological dynamics of Sheep/goat and Cattle brucellosis in the Chinese Southwest, 2006–2024.
Table 3.
Species/biovar composition, MLVA genotype diversity, and geographic distribution of Brucella melitensis isolates from the Chinese Southwest based on Literatures.
Fig 4.
Transboundary distributions of ST39 B. melitensis in the Chinses Southwest and Southeast Asia.
Note: The chord diagram displays the geographical distribution of reported ST39 B. melitensis isolates. The circular sectors represent different sequence types (STs), with the large central sector denoting ST8 isolates from mainland China (n = 1,229). The connecting chords between sectors indicate allelic differences among ST types, with line thickness proportional to genetic distance. The inset pie chart details 39 ST39 isolates from the Chinese Southwest and Southeast Asia regions, showing distribution by subregion: Yunnan, China (n = 19); Malaysia (n = 13); Guizhou, China (n = 10); Sichuan, China (n = 4); Thailand (n = 4).