About COVID-19 and AI Simulation Project

Simulation using AI and other technologies

 The COVID-19 AI Simulation Project explores the potential use of technology to balance economic activities with measures to prevent the spread of COVID-19.
We collect and analyze data on the early detection of the spread of infection using AI and other technologies, conduct simulations, develop new technologies that contribute to infection prevention measures, and verify the results for social implementation.

Scientific approach from multiple perspectives:

Approaches from multiple perspectives are necessary to predict and respond to uncertain events such as the recent COVID-19 pandemic. It is important to discuss and examine the validity of various models and verification methods, and to encourage further refinement by exchanging opinions among teams and models adopting different approaches.
For example, in predicting infection, it is common to quantitatively grasp the infection situation using an infectious disease mathematical model called SEIR. On the other hand, in the actual world, various infection types can occur according to complex human movements. Thus, we consider the multi-agent model and complex network theory also useful, dynamically linking various elements in analyzing how infection spreads.
In addition, it is important as a basic scientific attitude to update hypotheses and simulation assumptions in accordance with the latest data and situations that are constantly updated. In this project, discussions are held as needed among teams that adopt multiple approaches, and the latest results are announced promptly based on analyses that are tailored to the strengths and characteristics of each model.

Scenario-based review:

 In this project, instead of coming to a simple prediction, we analyze various scenarios such as “What could happen if … takes place.” In situations where the prediction itself causes people’s behavioral changes, it is important to consider multiple policies that accommodate various possibilities under multiple scenarios, rather than providing a single-scenario solution to the uncertain situation.
 The spread of COVID-19 is affected by a complex interplay of uncertain and diverse factors, such as the progress of vaccination, the amount of medical resources available, and the effects of declaring an emergency. The purpose of this project is to present materials for policy making assuming various scenarios using multiple analytical models.

Structure of this project:

 There is a need for a system that can quickly provide scientific evidence in response to ever-changing situations. In this project, individual R & D teams and Open Collaboration Partners (OCP) (hereinafter referred to as “Research Teams”) have been selected by open recruitment for each R & D area. In addition, from the viewpoint of promptly disclosing the research results and ensuring scientific validity, each expert committee member gives advice from a professional point of view for each individual R & D theme, and the progress of the research is monitored as needed. It is announced.

Preparing for future pandemics:

 Various measures are being taken to put an end to the novel coronavirus; however, the fight against the pandemic will not end with the novel coronavirus. To prepare for other pandemics in the future, it is important to accumulate and disclose analytical knowledge, leaving its traces in history.
 We know that real-time access to a variety of data is necessary to accurately analyze infection conditions and make meaningful simulations. Data on highly granular human flow density, human flow movements, genome sequences, international immigration status, cluster analysis, and medical resources are some of these examples. Through this project, we work on the construction of system to access such data, and organize the knowledge that contributes to discussions on the ideal pandemic countermeasures for national security purposes.




  • 2022年度リサーチクエスチョン1 新技術の活用

    2022年度リサーチクエスチョン1 新技術の活用



    SNS/Web 上の情報を基にAI 等を⽤いたデータ解析を⾏うことによって、感染症の流⾏・拡⼤の兆しをつかむ⽅策を提⽰できないか。
    AI 等を活用することで、従来の検査以外の手法によって新型コロナウイルス感染症の診断を行う手法を提示できないか。
  • 2022年度リサーチクエスチョン2 感染状況シミュレーション

    2022年度リサーチクエスチョン2 感染状況シミュレーション



    SNS/Web 上の情報を基に AI 等を活用したデータ解析を行うことによって、感染症の流行・拡大の兆しをつかむ方策を提示できないか。
  • 2022年度リサーチクエスチョン3 感染拡大・抑制シミュレーション

    2022年度リサーチクエスチョン3 下水サーベイランス技術の開発





  • 2021年度リサーチクエスチョン1 感染拡大の早期探知

    AI 等を用いたデータ解析によって感染症の流行・拡大を早期に探知する新技術の実現を目的とし研究開発を行います。

  • 2021年度リサーチクエスチョン2 感染防止シミュレーション


  • 2021年度リサーチクエスチョン3 感染拡大・抑制シミュレーション


  • 2021年度リサーチクエスチョン4 新技術導入



  • 2020年度リサーチクエスチョン1 「分野別ガイドラインの進化」のために必要な「室内気流シミュレーション」、「飛沫の見える化」


  • 2020年度リサーチクエスチョン2 「接触機会低減」のための「ICT、IoTの活用」(ウェアラブル機器を用いて接触回避などを行う)


  • 2020年度リサーチクエスチョン3 「検査効率化・信頼性向上」に必要な「PCR、抗体検査等の効果的組合せ」


  • 2020年度リサーチクエスチョン4 「第二波対策」として必要な「感染予測・対策の効果検証」(SIRモデルの代替となるモデルの確立)、「必要な医療リソース(病床・医療物資等)の需要予測と最適配置」


  • 2020年度リサーチクエスチョン5 「早期検知と重症化回避」のための「CTスキャン画像分析」、軽症者等のモニタリング、重症化リスク予測、ウイルス変異の影響の理解 等


*掲載された資料は、内閣官房が行った「COVID-19 AI・シミュレーションプロジェクト」において、コロナ対策の効果等の分析のため、各研究者がそれぞれモデルを構築して行ったシミュレーション結果等を説明するものです。この資料内で説明されるシミュレーション等の結果については、政府の公式の見解を示すものではありません。