Building a social protocol to avoid pandemics of influenza by relying artificial society approach
| Project/Area Number |
18K18924
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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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 | Kyushu University |
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Principal Investigator |
Tanimoto Jun 九州大学, 総合理工学研究院, 教授 (60227238)
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| Project Period (FY) |
2018-06-29 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥6,240,000 (Direct Cost: ¥4,800,000、Indirect Cost: ¥1,440,000)
Fiscal Year 2019: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
Fiscal Year 2018: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
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| Keywords | 感染症 / 季節性インフルエンザ / ワクチン接種ジレンマ / 数理疫学 / 進化ゲーム理論 / マルチエージェントシミュレーション / パンデミック / インフルエンザ / 人工社会システム |
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| Outline of Final Research Achievements |
A Multi Agent Simulation (MAS) model dovetailing evolutionary game theory with epidemiological dynamics based on SIR/V is established where various subsidizing policies for vaccination are considered. Relying on the presented model, we explore whether or not hub-agents priority policy letting them commit vaccination for free is meaningful to oppress disease spreading. We found that whether such a subsidy policy being justified is significantly dependent on whether an agent given a free-ticket is cooperator who originally intends to be self-financed vaccinee or defector who tries to free-ride.
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| Academic Significance and Societal Importance of the Research Achievements |
予防接種コストが小さく，かつ，補助金の総予算規模が小さきときには，ハブではなく，社会複雑ネットワークの隣人数が小さい周辺部のエージェントに優先的に補助金を給付する方が，寧ろ社会効率が高くなる．これは，次数の高いハブエージェントは，感染リスクを自ら認識し，自助努力で予防接種する可能性があるため，かれらに予防接種補助金を給付することは，大感染予防には意味があっても，社会的誘引効果によってミスから予防接種を行うエージェントを増やす社会ダイナミクスが一部で阻害される，つまり補助金によるワクチン接種の無駄うちが生じてしまうことによるものである．この発見は社会的処方箋をデザインする上で有意な知見である．
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Report
(3 results)
Research Products
(13 results)
