Effectiveness of the COVID-19 contact-confirming application (COCOA) based on multi-agent simulation

Yuto Omae, Jun Toyotani, Kazuyuki Hara, Yasuhiro Gon, Hirotaka Takahashi

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

As of Aug. 2020, coronavirus disease 2019 (COVID-19) is still spreading in the world. In Japan, the Ministry of Health, Labour and Welfare developed “COVID-19 Contact-Confirming Application (COCOA),” which was released on June 19, 2020. By utilizing COCOA, users can know whether or not they had contact with infected persons. If those who had contact with infected individuals keep staying at home, they may not infect those outside. However, effectiveness decreasing the number of infected individuals depending on the app's various usage parameters is not clear. If it is clear, we could set the objective value of the app's usage parameters (e.g., the usage rate of the total populations) and call for installation of the app. Therefore, we develop a multi-agent simulator that can express COVID-19 spreading and usage of the apps, such as COCOA. In this study, we describe the simulator and the effectiveness of the app in various scenarios. The result obtained in this study supports those of previously conducted studies.

Original languageEnglish
Pages (from-to)931-943
Number of pages13
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume25
Issue number6
DOIs
Publication statusPublished - Nov 2021

Keywords

  • Contact-Confirming Application
  • COVID-19
  • Multi-agent simulation
  • SEIR model

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