Effect of Social Distancing on COVID-19 Infection Determined by a Multi-agent Simulation

Abstract

COVID-19 has spread all over the world, and the cumulative number of infected people is still increasing dairy. Therefore, interventions to limit the spread of COVID-19 should be considered for each social situation. Effective interventions to minimize COVID-19 transmission vary for each situation in accordance with a quantitative framework called event R. Of those various events, the example of a school has the highest possibility of infection, but distancing as an intervention cuts the number of new infections in half. Therefore, perform we multi-agent simulation of COVID-19 transmission without measures and with social distancing. To perform simulation under the same conditions as those in the event R calculation, our simulation is performed under the following conditions: a single infected person enters the classroom with the first set of multiple uninfected people and a certain amount of time passes. After that, the infected person enters another classroom with the same number of new uninfected people, and the process is repeated eight times. The simulation results show that there is a relationship between social distancing and the spread of infection, but the rate of decrease is not constant.