Prediction of Epidemic of Infectious Disease by Statistical Modeling, and Scientific Policy Making
| Project/Area Number |
23650143
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Statistical science
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| Research Institution | The University of Tokyo |
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Principal Investigator |
IMOTO Seiya 東京大学, 医科学研究所, 准教授 (10345027)
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| Project Period (FY) |
2011 – 2013
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2013: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2012: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2011: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | 感染症シミュレーション / 予測モデル / エージェントベースシミュレーション / 予防 / 感染症拡大の数理モデル / マクロシミュレーション / エージェントシミュレーション / ジョイントモデル / 数理モデル / インフルエンザ / パンデミック |
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| Research Abstract |
Like Tokyo, we constructed virtual city including five areas on computer and 1.2 million residents, which are categorized into three attributes, company employees, students and non-workers, lives there. An agent-based simulation model was established to simulate epidemic at schools, companies or stores. We tuned the parameters in the simulation model so that the infectious rate becomes 30% and performed three types of vaccine deriver strategies; priority to company workers, people in house or random deriver. As the result, priority to company workers is the most effective.
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Report
(4 results)
Research Products
(11 results)
