著者

倉橋 節也 横幕 春樹 矢嶋 耕平 永井 秀幸

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.37, no.1, pp.C-L42_1-9, 2022-01-01 (Released:2022-01-01)

参考文献数

21

被引用文献数

2

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In this paper, we propose a new SEIR model for COVID-19 infection prediction using mobile statistics and evolutionally optimisation, which takes into account the risk of influx. The model is able to predict the number of infected people in a region with high accuracy, and the results of estimation in Sapporo City and Tokyo Metropolitan show high prediction accuracy. Using this model, we analyse the impact of the risk of influx to Sapporo City and show that the spread of infection in November could have been reduced to 0.6 if the number of influxes had been limited after the summer. We also examine the preventive measures called for in the emergency declaration in the Tokyo metropolitan area. We found that comprehensive measures are highly effective, and estimated the effect of vaccination and circuit breakers on the spread of infection after the spring of 2021 using the effective reproduction reduction rate of infection control measures obtained from the individual-based model and the SEIR model.