2022 Fiscal Year Final Research Report
Big data ecology for understanding host-pathogen interaction from sequence data
| Project/Area Number |
16H04845
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Ecology/Environment
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| Research Institution | Nagoya University (2020, 2022)
Kyushu University (2016-2019) |
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Principal Investigator |
Iwami Shingo 名古屋大学, 理学研究科, 教授 (90518119)
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| Co-Investigator(Kenkyū-buntansha) |
立木 佑弥 東京都立大学, 理学研究科, 助教 (40741799)
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| Project Period (FY) |
2016-04-01 – 2021-03-31
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| Keywords | 数理モデル / シミュレーション / データ解析 |
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| Outline of Final Research Achievements |
We conducted a study to predict the prevalence of infectious diseases by combining a mathematical model and its theory (theoretical ecology) described by a hybrid dynamical system with gene sequence information of pathogens (infectious disease epidemiology), which is accumulating as information worthy of big data, using a data assimilation method (computer science). Using a mathematical model and computer simulation based on the developed hybrid dynamical system, we analyzed the gene sequence data of seasonal influenza A that have been accumulated up to the present day. The complementary fusion of the theory of population dynamics and data assimilation methods historically used in ecology suggested the possibility of making breakthroughs in both data reproducibility and future prediction.
Translated with www.DeepL.com/Translator (free version)
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| Free Research Field |
数理生物学
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| Academic Significance and Societal Importance of the Research Achievements |
課題実施期間中に、COVID-19が出現したことを受け、開発したアプローチを発展させて、SARS-CoV-2の進化動態を予測する進化シミュレータの開発にも着手した。早期の流行予測により、医療体制の整備、個人保護具・抗ウイルス薬の最適分配、被害規模の推定等が可能になれば、感染症対策を大きく改善する事に貢献できる。
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