CIRO: COVID-19 infection risk ontology

TL;DR

This paper introduces CIRO, an ontology-based system that automates COVID-19 infection risk assessment using RDF and SPARQL, aiming to assist public health officials and improve contact tracing efficiency.

Contribution

The study develops the COVID-19 Infection Risk Ontology (CIRO) to automate infection risk assessment based on Japanese government guidelines, integrating knowledge representation and reasoning.

Findings

Knowledge graph can infer infection risks as formulated by the government.

Reasoning experiments show the system's computational efficiency.

The approach demonstrates potential to reduce manual labor in contact tracing.

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.