Modeling mumps incidence in China: spatiotemporal clusters and evolving risk factors (2005-2020)

Mumps remains a major public health challenge in China, exhibiting distinct seasonal peaks in spring and notable spatial heterogeneity in incidence patterns. These spatiotemporal characteristics necessitate advanced analytical methods to identify driving factors and inform targeted intervention strategies.

We integrated space-time scanning statistics and geographically and temporally weighted regression (GTWR) to analyze mumps incidence across China (2005-2020). This approach overcomes the limitations of traditional methods by simultaneously assessing economic development, education level, population structure, and healthcare resources factors.

National incidence exhibited a fluctuating decline from 27.60 (2005) to 10.09 per 100,000 (2020), peaking in 2012 (38.49 per 100,000). Space-time scanning identified persistent high-risk clusters in western China and transient clusters in northeastern regions. GTWR modeling revealed significant spatiotemporal variations in risk factors: illiteracy rate and population density showed transitioning impacts reflecting improved health education, while household size effects strengthened, emphasizing close-contact transmission. Healthcare resources exhibited opposing effects, being protective in eastern regions but risk-enhancing in western areas. GDP per capita demonstrated protective effects in western and southeastern China but was associated with elevated risk elsewhere.

The findings underscore the need for regionally tailored prevention strategies and precision interventions accounting for local socioeconomic contexts. This study provides a methodological framework for spatiotemporal disease surveillance and evidence-based policy-making to reduce mumps transmission in China.