地域への新型コロナウイルス感染者流入リスクとワクチン効果の影響評価

• 倉橋 節也

  筑波大学ビジネスサイエンス系

• 横幕 春樹

  NTT データ数理システム

• 矢嶋 耕平

  筑波大学大学院システム情報工学研究群

• 永井 秀幸

  京都美術工芸大学

書誌事項

タイトル別名

• Assessment of the Impact of COVID-19 Infections Considering Risk of Infected People Inflow to the Region and the Vaccination Effect

抄録

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.

収録刊行物

• 人工知能学会論文誌

  人工知能学会論文誌 37 (1), C-L42_1-9, 2022-01-01

  一般社団法人 人工知能学会

キーワード

• COVID-19
• individual-based model
• SEIR model
• vaccination effect
• new coronavirus variants

詳細情報

• CRID: 1390853567321756288
• NII論文ID: 130008139225
• DOI: 10.1527/tjsai.37-1_c-l42
• ISSN: 13468030
• ISSN: 13460714
• Web Site: https://www.jstage.jst.go.jp/article/tjsai/37/1/37_37-1_C-L42/_pdf
• 本文言語コード: ja
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可