Application of Graphs in a One Health Framework

Area of Science:

• Public Health
• Environmental Science
• Veterinary Medicine
• Systems Biology

Background:

• The One Health framework emphasizes the interconnectedness of human, animal, and environmental health.
• Emerging public health threats like zoonotic diseases and antibiotic resistance necessitate integrated approaches.
• Graph theory has existing applications in health sciences, applicable across various One Health levels.

Purpose of the Study:

• To explore the application of process graphs (P-graphs) within the One Health framework.
• To investigate the use of P-graphs for analyzing complex health issues at the ecosystem level.
• To demonstrate the utility of P-graphs and their algorithms for information extraction and intervention planning.

Main Methods:

• Utilizing process graphs (P-graphs) from chemical engineering.
• Applying combinatorial algorithms associated with P-graphs.
• Implementing these methods in the R statistical programming language.
• Researching the application of P-graphs for ecosystem-level health analysis.

Main Results:

• P-graphs provide a structured method for understanding complex systems within One Health.
• Combinatorial algorithms can extract crucial information for identifying key intervention points.
• The R implementation facilitates practical application of P-graphs in One Health research.
• This approach aids in identifying critical factors in ecosystem-level health challenges.

Conclusions:

• Process graphs offer a novel and effective tool for One Health research and practice.
• The integration of P-graphs enhances the ability to analyze and manage complex health issues.
• This methodology supports data-driven decision-making for public health interventions.