[1M1-OS-20a] 社会現象とAIと可視化(1/3)

[1M1-OS-20a-02] Influence of Network Structure on Herd Immunity in Infectious Diseases

〇Ryoya Watanabe¹, Fumito Ihara¹, Naoki Wakabayashi², Takumi Sugiura¹, Daiki Takamura¹, Satoshi Kurihara² (1. Graduate School of Science and Technology, Keio University, 2. Faculty of Science and Technology, Keio University)

Keywords:Network Science, Computational Social Science, COVID-19, Multi-agent Simulation

By constructing a multi-agent simulation in which an agent is a person who moves based on a power law and the environment is a city connected by a network relationship, we discussed the phenomenon of COVID-19 infection. Mathematical models, such as the SIR model, have been used to elucidate the phenomenon of COVID-19 infection, but although they are highly interpretable, they do not reflect reality because they assume a homogeneous model of people's contact networks. By conducting simulations using a realistic network model, we examined vaccination and herd immunity. The hypotheses about the urban network obtained by observing real-life infection phenomena were tested by comparing several scenarios in the simulation. The properties obtained in this study can be applied not only to COVID-19 but also to other infectious diseases, and by comparing them with mathematical models, we believe that our understanding of infectious disease transmission will be enhanced.