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Modeling the drivers of mumps incidence in China: a spatial multi-method analysis
Ke Hu1, Xingjin Yang2, Shuiping Ou3
1Xiamen Haicang Hospital, Xiamen, Fujian, China.
Frontiers in Public Health
|June 4, 2026
Summary
Mumps incidence in China shows distinct spatial patterns. Geographically Weighted Regression (GWR) effectively identified region-specific drivers, informing targeted public health interventions for mumps prevention.
Area of Science:
- Epidemiology
- Spatial Analysis
- Public Health
Background:
- Mumps presents a significant public health challenge in China, with notable spatial variations in incidence.
- Previous studies often used global or single-scale models, hindering the identification of region-specific determinants for mumps.
- Understanding multi-scale driving factors is crucial for effective, targeted public health interventions against mumps.
Purpose of the Study:
- To systematically explore the multi-scale driving factors of mumps incidence across China.
- To overcome the limitations of traditional global models by employing an integrated multi-model analytical framework.
- To identify region-specific determinants influencing mumps distribution and inform tailored prevention strategies.
Main Methods:
- Utilized provincial-level data from China for 2020.
- Employed spatial autocorrelation analysis to detect mumps incidence clustering.
- Integrated multiple linear regression (MLR), spatial lag model (SLM), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR) for comprehensive analysis.
Main Results:
- Mumps incidence displayed a west-high/east-low gradient with significant spatial autocorrelation.
- Geographically Weighted Regression (GWR) demonstrated superior performance over MGWR in this small-sample setting, capturing local spatial heterogeneity effectively.
- Key drivers like GDP per capita, education, healthcare access (general practitioner density), air quality (PM2.5), and child dependency ratio exhibited spatially varying effects on mumps incidence.
Conclusions:
- Geographically Weighted Regression (GWR) is a robust method for analyzing spatial heterogeneity in mumps incidence, especially in small-sample contexts.
- Findings highlight the spatially varying influence of socioeconomic, healthcare, environmental, and demographic factors on mumps.
- Results provide a scientific basis for developing regionally differentiated mumps prevention and control strategies.
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