Towards a geocomputational landscape epidemiology: surveillance, modelling, and interventions

Area of Science:

• Environmental health
• Spatial epidemiology
• Geocomputation

Background:

• Landscape epidemiology uses environmental data to understand disease patterns.
• New data sources (citizen sensors, satellite imagery) and computational methods offer enhanced analysis capabilities.
• Existing approaches are often study-specific, hindering integration into operational tools.

Purpose of the Study:

• To propose a novel framework for geocomputational landscape epidemiology.
• To categorize existing methods for spatial epidemiology research.
• To demonstrate the framework's utility through a case study.

Main Methods:

• Developed a four-phase framework: characterizing assemblages, functions, interdependencies, and outcomes.
• Categorized existing geocomputational methods relevant to spatial epidemiology.
• Applied the framework to a leptospirosis case study in Sri Lanka.

Main Results:

• The proposed framework structures and interprets spatial epidemiology analyses effectively.
• The Sri Lanka case study validated the framework's practical application.
• The framework facilitates the integration of diverse data and methods.

Conclusions:

• The geocomputational landscape epidemiology framework provides a structured approach to complex spatial disease studies.
• This framework enhances the integration of environmental and health data for public health.
• It offers a valuable tool for researchers in spatially explicit disease-landscape studies.