Provincial Mumps Incidence and Drivers in China, 2020

Provincial-level data from China in 2020 was used to model the spatial drivers of mumps incidence. The analysis employed a multi-model framework integrating multiple linear regression, spatial lag, and geographically weighted regression. Results show significant spatial autocorrelation and a west-high/east-low gradient in mumps incidence.

Use Cases

• Modeling spatial heterogeneity of disease incidence based on provincial-level data.
• Comparing global versus local regression techniques for public health data.
• Analyzing the influence of socioeconomic and environmental factors on mumps spread.
• Identifying region-specific public health intervention targets based on variable effects.

Strengths

• Data is from 2020, described as the most recent complete year with publicly available multi-source data at the time of analysis.
• Analysis incorporates multidimensional data including socioeconomic factors, education, healthcare resources, population structure, and environmental factors.
• Model comparisons are provided with specific metrics like R-squared and AIC values.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The 25.0 KB file size suggests a very limited scope, likely containing only summary or model results rather than raw observational data.

Provenance

Source

figshare, author Ke Hu.

Collection Method

Provincial-level data from China was analyzed using spatial autocorrelation and a multi-model regression framework.

Time Range

2020

Freshness

Last updated 2026-05-19 05:29:05; freshness should be verified.

Geography

China, provincial-level.