Integrating disease mapping and flexible scan statistics to identify and visualize spatial clusters

Area of Science:

• Epidemiology
• Geographic Information Systems (GIS)
• Biostatistics

Background:

• Accurate spatial disease characterization is crucial for understanding disease origins and implementing effective public health strategies.
• Identifying disease hotspots and understanding their spatial patterns are key challenges in public health research.

Purpose of the Study:

• To develop and validate an integrated analytical framework for disease mapping and spatial cluster detection.
• To improve the accuracy and reliability of identifying and localizing disease high-risk areas.

Main Methods:

• An "exploration-validation-refinement" workflow combining qualitative visualization (choropleth maps) and quantitative spatial analysis (flexible scan statistics - FleXScan).
• Utilized dot cartograms to analyze internal heterogeneity within clusters, accounting for population density.
• Validated the framework using synthetic datasets and real-world measles case data.

Main Results:

• The integrated framework demonstrated improved accuracy and reliability in detecting spatial disease clusters.
• Successfully localized specific high-risk cores within identified disease clusters.
• Provided actionable insights for precision disease control strategies.

Conclusions:

• The developed framework offers a robust approach for spatial disease analysis and cluster identification.
• Enhances the ability to guide targeted public health interventions and optimize resource allocation.
• Facilitates a deeper understanding of disease etiology through precise spatial characterization.