Determination of optimal prevention strategy for COVID-19 based on multi-agent simulation

This study proposes a direction for the utilization of multi-agent simulation (MAS) to consider an optimal prevention strategy for the spread of the coronavirus disease of 2019 (COVID-19) through a pandemic modeling example in Japan. MAS can flexibly express macroscopic phenomena formed through the interaction of micro-agents modeled to act autonomously. The use of MAS can provide a variety of recommendations for bringing a pandemic under control, even in the case of the COVID-19 pandemic, which has become more intense as of 2021. However, models that do not consider individual heterogeneity, such as analytical Susceptible–Exposed–Infectious–Recovered (SEIR) models, are often used as predictive models for infectious diseases and the main reference for decision-making. In this study, we show that by constructing a MAS that simulates a metropolitan city in Japan in a simple manner while considering the heterogeneity of age and other background information, we can capture the effects of various measures such as vaccinations on the spread of infections in a more realistic setting. Moreover, it is possible to offer various recommendations for optimal strategies to suppress a pandemic by combining reinforcement learning with MAS. This study explicates the potential of MAS in the development of strategies to prevent the spread of infection.