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A survey of epidemic management data models
Yongchao Gao1, Qiuyue Wang2, Mark S Fox3
1Shandong Computer Science Center, Qilu University of Technology, Jinan, China.
Health Informatics Journal
|May 30, 2023
Summary
The COVID-19 pandemic highlighted the need for better data interoperability in public health emergencies. While existing ontologies cover many epidemic concepts, gaps remain, requiring further development for comprehensive data sharing.
Area of Science:
- Public Health Informatics
- Data Science
- Knowledge Representation
Background:
- The COVID-19 pandemic underscored the critical role of data in managing public health emergencies.
- Increased awareness of ontologies' importance in creating precise data models for enhanced data interoperability among stakeholders.
Purpose of the Study:
- To survey vocabularies and ontologies relevant to achieving epidemic-related data interoperability.
- To identify common and missing knowledge patterns in existing resources based on use cases.
Main Methods:
- Review of 16 vocabularies and ontologies.
- Analysis of common knowledge patterns across resources.
- Gap analysis based on defined use cases.
Main Results:
- Existing vocabularies and ontologies offer substantial coverage of concepts pertinent to epidemic use cases.
- Identified gaps in conceptual coverage within the surveyed resources.
Conclusions:
- Current ontologies provide significant, but incomplete, support for epidemic data interoperability.
- Further work is necessary to address identified gaps and enhance conceptual coverage for robust emergency data management.
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