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CIRO: COVID-19 infection risk ontology
Shusaku Egami1, Yasunori Yamamoto2, Ikki Ohmukai3
1Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Koto, Tokyo, Japan.
Insights
This study introduces the COVID-19 Infection Risk Ontology (CIRO) to automate infection risk assessment for public health officials. CIRO uses knowledge graphs and SPARQL queries to manage COVID-19 risks efficiently.
Area of Science:
- Public Health
- Informatics
- Epidemiology
Background:
- Contact tracing is crucial for managing contagious diseases like COVID-19.
- Manual contact tracing during the COVID-19 pandemic placed a significant burden on public health officials.
- Japan implemented manual contact tracing, highlighting the need for efficient solutions.
Purpose of the Study:
- To develop an automated system for assessing individual COVID-19 infection risk.
- To reduce the manual labor required by public health officials during contact tracing.
- To create a computable representation of COVID-19 infection risk factors.
Main Methods:
- Development of the COVID-19 Infection Risk Ontology (CIRO) using Resource Description Framework (RDF).
- Utilization of SPARQL Protocol and RDF Query Language (SPARQL) for querying the knowledge graph.
- Construction of a knowledge graph representing COVID-19 infection risks based on government guidelines.
Main Results:
- The knowledge graph successfully inferred infection risks as formulated by the government.
- Reasoning experiments demonstrated the computational efficiency of the knowledge processing approach.
- The study validated the usefulness of the ontology for automated risk assessment.
Conclusions:
- The CIRO provides a valuable tool for automating COVID-19 infection risk assessment.
- Knowledge graph technology can significantly support public health initiatives.
- Further research is needed to address remaining challenges for full deployment.
Abstract:
Public health authorities perform contact tracing for highly contagious agents to identify close contacts with the infected cases. However, during the pandemic caused by coronavirus disease 2019 (COVID-19), this operation was not employed in countries with high patient volumes. Meanwhile, the Japanese government conducted this operation, thereby contributing to the control of infections, at the cost of arduous manual labor by public health officials. To ease the burden of the officials, this study attempted to automate the assessment of each person's infection risk through an ontology, called COVID-19 Infection Risk Ontology (CIRO). This ontology expresses infection risks of COVID-19 formulated by the Japanese government, toward automated assessment of infection risks of individuals, using Resource Description Framework (RDF) and SPARQL (SPARQL Protocol and RDF Query Language) queries. For evaluation, we demonstrated that the knowledge graph built could infer the risks, formulated by the government. Moreover, we conducted reasoning experiments to analyze the computational efficiency. The experiments demonstrated usefulness of the knowledge processing, and identified issues left for deployment.
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