Abstract
In this paper, we propose a new SEIR model for COVID-19 infection prediction using mobile statistics and evolutionary 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 less than a half if the number of influxes had been limited after the summer. We also examine the preventive measures called for in the emergency declaration of the Tokyo metropolitan area. We found that comprehensive measures are highly effective using the effective reproduction reduction rate of infection control measures obtained from the individual-based model and the SEIR model. We also estimated the effect of vaccination and circuit breakers.
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Kurahashi, S. (2023). Assessment of the Impact of COVID-19 Infections Considering Risk of Infected People Inflow to the Region. In: Yada, K., Takama, Y., Mineshima, K., Satoh, K. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2021. Lecture Notes in Computer Science(), vol 13856. Springer, Cham. https://doi.org/10.1007/978-3-031-36190-6_26
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DOI: https://doi.org/10.1007/978-3-031-36190-6_26
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