Harnessing the Core Propagation Phenomenon Ontology to Develop a Knowledge Graph for Tracking Health-Related...

Area of Science:

• Biomedical informatics
• Public health surveillance
• Data science

Background:

• Biomedical data analysis and visualization often require specialized data experts for unique health events.
• Current tools lack automatic semantic visualization for tracking health risk propagation.
• Rapid spread of diseases like COVID-19 and Monkeypox highlights the need for timely data analysis for governmental decision-making.

Purpose of the Study:

• To propose a knowledge graph (KG) design for the spatio-temporal tracking of public health event propagation.
• To develop an automated approach for semantic visualization of health risk spread.
• To create a specialized domain ontology for health-related propagation phenomena.

Main Methods:

• Specialization of the Core Propagation Phenomenon Ontology (PropaPhen) into a health-specific domain ontology.
• Instantiation of the proposed knowledge graph using data from the Unified Medical Language System (UMLS) and OpenStreetMap.
• Development of a use case analyzing COVID-19 data from the World Health Organization (WHO).

Main Results:

• Demonstration of a novel knowledge graph framework for public health event tracking.
• Evaluation of the proposed ontology's applicability in a real-world health crisis scenario (COVID-19).
• Analysis of spatio-temporal patterns in disease propagation using the developed KG.

Conclusions:

• The proposed knowledge graph approach offers a viable solution for semantic visualization of public health event propagation.
• This methodology can enhance timely decision-making during epidemics and pandemics.
• Integration with existing data sources like UMLS and OpenStreetMap facilitates practical implementation.