| 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 |
FY2021 saw many advancements toward the objectives of the program. The PI published works about the co-evolution of sentiments, behavior, and disease spreading using social media data and a seminal study of Chinese mobility in the first year of Covid-19. Another work by the PI concerned how to represent contact data more efficiently for network epidemiology. Co-I Masuda contributed to several theoretical studies of disease spreading and how to analyze epidemic data. Co-I Ibuka had several publications about Covid-19 in Japan, and its social implications, including a seminal article about movement patterns during the Olympic games. The latter paper was co-authored by co-I Asai, who also co-authored a great number of reports about Covid-19 in Japan. Co-I Beauchemin published work on in-host viral dynamics. Co-I Takayasu published theoretical works on spreading dynamics and data science papers about opinion dynamics.
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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
Our work is continuing along the lines of the proposal. Since Covid-19 has become a real-life laboratory for many of the research ideas of the proposal, some of the work has been changed to follow these developments. For example, Co-I Asai has been heavily involved in monitoring the impact of Covid-19 on Japanese healthcare. Data-driven studies of the Covid-10 epidemics have replaced some of the original plan's emphasis on theoretical and computational modeling, but on the whole, we are covering the research initially anticipated.
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| Strategy for Future Research Activity |
In the future, we aim to extend the scope of our project and delve deeper into the co-evolution of awareness and epidemics, as informed by data. This year, we plan to continue working with Covid-19 data to improve to match reality better. We will refine our network epidemiological vaccination models based on the new data by incorporating experimental game theory. Specifically, we will continue our work on models of various health decisions using game theory and their impact using network epidemiology. We will also explore vaccination games on networks to develop more realistic models. To achieve this, we will simulate different scenarios and identify optimal vaccination strategies using our previously developed model.
Prof. Holme and his team will continue to lead the core analysis, including calibrating our models and integrating results from the different work packages. In parallel to these efforts, we will focus on unifying the outcomes from the work packages to build a comprehensive network-epidemiological theory of the co-evolution of epidemics and behavior.
Additionally, we will explore the impact of different intervention strategies on the spread of epidemics. We will investigate the efficacy of contact tracing, quarantine, and vaccination in reducing the transmission of infectious diseases.
Finally, we aim to provide policymakers with actionable insights and recommendations for designing effective intervention strategies to curb the spread of infectious diseases.
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