| Project/Area Number |
15K16061
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Soft computing
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| Research Institution | Tohoku University (2018)
The University of Tokyo (2015-2017) |
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Principal Investigator |
Fujiwara Naoya 東北大学, 情報科学研究科, 准教授 (00637449)
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| Project Period (FY) |
2015-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | 複雑ネットワーク / 感染症 / 数理モデル / 人流データ |
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| Outline of Final Research Achievements |
In this research, we employed human mobility data in urban areas to construct time-dependent contact networks, performed simulations of spread of an infectious disease on the contact networks, and derived an approximated equation to estimate the final size of the infection. By varying a control parameter for the contact network, we found that the system shows the percolation transition and the scaling behavior. In the approximation, we can use small numbers of eigenvalues and eigenvectors of the contact networks, and found that the numerical solution of this equation well describes the simulation results with respect to the final size. Therefore,the relevant features of the spreading pattern can be captured by the eigenvectors of the connectivity matrices of the contact networks.
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| Academic Significance and Societal Importance of the Research Achievements |
実データ解析によってネットワークを構成するとともに、大規模な感染シミュレーション、そして理論を用いて感染拡大を見積もるとともに感染症対策への提言も視野に入れている点が本研究の大きな特徴である。現実のネットワークにおける知見を得ることが困難であった従来の近似手法を改善して、実データ解析にも耐えうる解析手法を整備、提供することができた。
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