| Project/Area Number |
21H04595
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Medium-sized Section 25:Social systems engineering, safety engineering, disaster prevention engineering, and related fields
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| Research Institution | Kobe University |
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Principal Investigator |
Holme Petter 神戸大学, 計算社会科学研究センター, リサーチフェロー (50802352)
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| Co-Investigator(Kenkyū-buntansha) |
高安 美佐子 東京工業大学, 情報理工学院, 教授 (20296776)
井深 陽子 慶應義塾大学, 経済学部(三田), 教授 (20612279)
上東 貴志 神戸大学, 計算社会科学研究センター, 教授 (30324908)
増田 直紀 神戸大学, 計算社会科学研究センター, リサーチフェロー (40415295)
浅井 雄介 国立研究開発法人国立国際医療研究センター, 国際感染症センター, 研究員 (70779991)
Beauchemin Catherine 国立研究開発法人理化学研究所, 数理創造プログラム, 副プログラムディレクター (70898931)
村田 剛志 東京工業大学, 情報理工学院, 教授 (90242289)
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| Project Period (FY) |
2021-04-05 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2024)
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| Budget Amount *help |
¥41,990,000 (Direct Cost: ¥32,300,000、Indirect Cost: ¥9,690,000)
Fiscal Year 2024: ¥8,580,000 (Direct Cost: ¥6,600,000、Indirect Cost: ¥1,980,000)
Fiscal Year 2023: ¥10,400,000 (Direct Cost: ¥8,000,000、Indirect Cost: ¥2,400,000)
Fiscal Year 2022: ¥11,050,000 (Direct Cost: ¥8,500,000、Indirect Cost: ¥2,550,000)
Fiscal Year 2021: ¥11,960,000 (Direct Cost: ¥9,200,000、Indirect Cost: ¥2,760,000)
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| Keywords | Network epidemiology / Network science / 理論的疫学 / Game theory / Behavioral modeling / Theoretical epidemiology |
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| Outline of Research at the Start |
Emergent epidemic outbreaks are complex challenges for social systems engineering. To engineer effective interventions, we need to model the feedback between health behavior and epidemics. Interventions affect the epidemics either by altering the contact structures between people or the susceptibility of individuals. Higher-order network models can capture both these aspects. This project will use simulations, mathematical modeling, experimental game theory, and insights from the COVID-19 pandemics to incorporate behavioral feedbacks into network epidemiology.
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| Outline of Annual Research Achievements |
During FY 2022, we published several papers on the core topics of this research program. For example, we published a paper about how to use previous observations of influenza for forecasting COVID-19 outbreaks and how to automate the extraction of epidemic-relevant information from doctors' reports by AI. Other works covered general models of spreading processes and how to balance breadth and depth in contact tracing if the goal is to find the source of an infection.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
This project follows the outline of the original research program relatively closely. Some advances we made were beyond the scope we could originally predict, such as the use of language models for data extraction. Others are maybe slightly altered. We do, for example, not now intend to build models of behavioral feedback on temporal network models (since these assume data of human mobility that has a much higher resolution than available. Rather, we use somewhat more mainstream, aggregated models.
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| Strategy for Future Research Activity |
Our research follow the general research trends. The results from the present project is pointing towards integration with models from artificial intelligence and more information-rich representations.
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