Analysis of brucellosis incidence and influencing factors in Yunnan Province based on multi-scale geographically weighted poisson regression model

ZHANG Lele; LI Ke; YUAN Rui; WANG Peng; YANG Xiangdong; YU Binbin; ZHANG Zhijie

doi:10.16462/j.cnki.zhjbkz.2025.08.004

Volume 29 Issue 8

Aug. 2025

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2025 > 29(8): 889-893

ZHANG Lele, LI Ke, YUAN Rui, WANG Peng, YANG Xiangdong, YU Binbin, ZHANG Zhijie. Analysis of brucellosis incidence and influencing factors in Yunnan Province based on multi-scale geographically weighted poisson regression model[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2025, 29(8): 889-893. doi: 10.16462/j.cnki.zhjbkz.2025.08.004

Citation:

ZHANG Lele, LI Ke, YUAN Rui, WANG Peng, YANG Xiangdong, YU Binbin, ZHANG Zhijie. Analysis of brucellosis incidence and influencing factors in Yunnan Province based on multi-scale geographically weighted poisson regression model[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2025, 29(8): 889-893. doi: 10.16462/j.cnki.zhjbkz.2025.08.004

Citation:

ZHANG Lele, LI Ke, YUAN Rui, WANG Peng, YANG Xiangdong, YU Binbin, ZHANG Zhijie. Analysis of brucellosis incidence and influencing factors in Yunnan Province based on multi-scale geographically weighted poisson regression model[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2025, 29(8): 889-893. doi: 10.16462/j.cnki.zhjbkz.2025.08.004

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Analysis of brucellosis incidence and influencing factors in Yunnan Province based on multi-scale geographically weighted poisson regression model

doi: 10.16462/j.cnki.zhjbkz.2025.08.004

ZHANG Lele¹,
LI Ke¹,
YUAN Rui²,
WANG Peng^{3, 4},
YANG Xiangdong^{3, 4},
YU Binbin^{3, 4
,
,},
ZHANG Zhijie^{1, 2
,
,}

1.
Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai 200032, China
2.
Department of Epidemiology and Health Statistics, Fudan University, Shanghai 200032, China
3.
Yunnan Provincial Institute for Endemic Disease Control and Prevention, Kunming 650500, China
4.
Yunnan Key Laboratory of Natural Focal Disease Prevention and Control Technology, Kunming 650500, China

ZHANG Lele and LI Ke contribute equally to this article

Funds:

National Natural Science Foundation of China 82473736

Natural Science Foundation of Shanghai 24ZR1414700

More Information

Corresponding author: YU Binbin, E-mail: 37593392@qq.com; ZHANG Zhijie, E-mail: epistat@gmail.com
Received Date: 2025-03-17
Rev Recd Date: 2025-06-09
Publish Date: 2025-08-10

Abstract

Abstract

Objective To analyze the influencing factors of brucellosis incidence in various districts of Yunnan Province in 2022, and to reveal the spatial heterogeneity of their effects, so as to provide a reference for formulating scientific prevention and control measures in different regions. Methods Data on brucellosis incidence and related influencing factors were collected from 129 districts in Yunnan Province in 2022. The spatial autocorrelation of the brucellosis incidence was analyzed. The goodness of fit of the generalized linear Poisson model (GLM-Poisson), geographically weighted poisson regression (GWPR) model and multi-scale geographically weighted poisson regression (MGWPR) model was compared. Based on the optimal model, influencing factors of brucellosis incidence were analyzed. Results In 2022, 1 015 brucellosis cases were reported in Yunnan Province, showing an east-high-west-low spatial distribution pattern with spatial autocorrelation. The MGWPR model had the highest goodness of fit [percent deviance explained (D²)=0.77, Akaike′s information criterion corrected(AICc)=718.27, root mean square error (RMSE)=9.79]. Model results showed that sheep stock (β=0.13-1.49), cattle stock (β=-0.28-0.72), pig stock (β=0.23-0.31), GDP per capita (β=-0.66-1.29), proportion of primary industry GDP (β=-0.83-0.47), grassland area (β=-0.69-1.66), and annual precipitation (β=-1.31-0.40) had significant influences on the incidence of brucellosis at local or global scales. The effects of different factors were heterogeneous across different regions of Yunnan. Conclusions The MGWPR model performs better in addressing the spatial heterogeneity of regression relationships and is more suitable for exploring influencing factors of brucellosis in Yunnan. Various factors such as socioeconomic and natural factors significantly influence brucellosis incidence with spatial variations, suggesting that region-specific prevention measures should be formulated.
- Brucellosis,
- Multi-scale geographically weighted poisson regression model,
- Spatial heterogeneity

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References(23)

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