Analysis on human interaction by Nonlinear time series analysis toward the prediction of infectious disease
| Project/Area Number |
16K16126
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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 | Tokyo University of Science |
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Principal Investigator |
Shimada Yutaka 東京理科大学, 工学部情報工学科, 助教 (50734414)
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| Project Period (FY) |
2016-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | 複雑ネットワーク / グラフ間距離 / 非線形時系列解析 / テンポラルネットワーク / テンポラル・ネットワーク / 応用数学 / 複雑系 / ネットワーク科学 |
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| Outline of Final Research Achievements |
We proposed a method for analyzing human interaction data by using a nonlinear time series analysis, where we incorporated the graph distance for complex networks into the conventional nonlinear time series analysis. Using this method, we elucidated the underlying properties of these data. We also applied our method to the prediction of infectious disease, and showed its predictability.
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Report
(3 results)
Research Products
(13 results)
