Effectiveness of the COVID-19 Contact-Confirming Application (COCOA) based on a Multi Agent Simulation

TL;DR

This study uses a multi-agent simulation to evaluate the effectiveness of Japan's COVID-19 contact-tracing app COCOA in reducing infections, providing insights into how app usage impacts disease spread.

Contribution

The paper introduces a multi-agent simulation model to assess COVID-19 contact-tracing app effectiveness, offering scenario-based analysis of app usage parameters.

Findings

Higher app usage rates reduce infection spread.

Simulation results align with previous studies.

Effectiveness depends on user adoption levels.

Abstract

As of Aug. 2020, coronavirus disease 2019 (COVID-19) is still spreading in the world. In Japan, the Ministry of Health, Labor, and Welfare developed "COVID-19 Contact-Confirming Application (COCOA)," which was released on Jun. 19, 2020. By utilizing COCOA, users can know whether or not they had contact with infected persons. If those who had contact with infectors keep staying at home, they may not infect those outside. However, effectiveness decreasing the number of infectors 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.