Evidence-Based Health Intelligence with Globally Localized Epidemic Knowledge Base: Merging Pathological Data,...

Area of Science:

• Epidemiology
• Public Health
• Data Science

Background:

• Infectious disease prevalence and fatalities are increasing globally.
• Disparities in outcomes exist across regions, socio-economic statuses, and demographics.
• Existing surveillance systems may lack comprehensive data integration.

Purpose of the Study:

• To propose a novel knowledge base for enhanced epidemic surveillance.
• To develop a system for efficient and optimal disease outbreak management.
• To provide tailored intervention advice for epidemic control.

Main Methods:

• Combined pathological, socio-environmental, and white paper data.
• Developed the Globally Localized Epidemic Knowledge base (GLEK).
• Utilized heuristic multi-locus data source integration.

Main Results:

• GLEK effectively merges diverse data streams (social, environmental, pathological, governmental).
• The system provides efficient advice for epidemic control and intervention.
• GLEK identifies optimal, tailored interventions based on integrated data.

Conclusions:

• The proposed GLEK offers a robust framework for epidemic surveillance.
• Integrated data analysis enables more effective and localized disease management.
• GLEK has the potential to improve public health responses to infectious diseases.