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| 講演名 | 2025-11-04 13:50 ［ポスター講演］A Study on a Method for Analyzing Epidemic Processes on Networks Using Dynamic Mode Decomposition ○Yuta Nishioka・Chisaki Yoneda・Yusuke Sakumoto（Kwansei Gakuin Univ.）  IA2025-43 |
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| 抄録 | （和） | In recent years, growing concerns over the spread of emerging infectious diseases and the dissemination of fake news have intensified interest in epidemic processes on networks. However, these processes are complex phenomena involving various intertwined factors such as individual behaviors and attributes, making their underlying mechanisms difficult to analyze. For a comprehensive understanding of epidemic dynamics, it is not sufficient merely to predict the scale or speed of propagation; rather, it is essential to elucidate the fundamental mechanisms driving such processes. Numerous approaches have been proposed to analyze epidemic processes on networks, including those based on mathematical modeling and machine learning. Among mathematical models, the Susceptible-Infected-Recovered (SIR) model is one of the most representative. Since the SIR model is formulated as a set of nonlinear equations, it is often linearized for analysis, which may lead to a loss of accuracy. On the other hand, machine learning approaches such as Long Short-Term Memory~(LSTM) networks and Gated Recurrent Units~(GRUs) can achieve high prediction accuracy from large datasets, but they are generally less effective for understanding the mechanisms underlying infection spread. To address these issues, this paper investigates a method for analyzing epidemic processes on networks using Dynamic Mode Decomposition~(DMD). DMD can not only provide accurate predictions even for complex systems involving nonlinear dynamics, but also extract important spatial modes that are useful for efficiently understanding the mechanisms of the phenomena. |
| （英） | In recent years, growing concerns over the spread of emerging infectious diseases and the dissemination of fake news have intensified interest in epidemic processes on networks. However, these processes are complex phenomena involving various intertwined factors such as individual behaviors and attributes, making their underlying mechanisms difficult to analyze. For a comprehensive understanding of epidemic dynamics, it is not sufficient merely to predict the scale or speed of propagation; rather, it is essential to elucidate the fundamental mechanisms driving such processes. Numerous approaches have been proposed to analyze epidemic processes on networks, including those based on mathematical modeling and machine learning. Among mathematical models, the Susceptible-Infected-Recovered (SIR) model is one of the most representative. Since the SIR model is formulated as a set of nonlinear equations, it is often linearized for analysis, which may lead to a loss of accuracy. On the other hand, machine learning approaches such as Long Short-Term Memory~(LSTM) networks and Gated Recurrent Units~(GRUs) can achieve high prediction accuracy from large datasets, but they are generally less effective for understanding the mechanisms underlying infection spread. To address these issues, this paper investigates a method for analyzing epidemic processes on networks using Dynamic Mode Decomposition~(DMD). DMD can not only provide accurate predictions even for complex systems involving nonlinear dynamics, but also extract important spatial modes that are useful for efficiently understanding the mechanisms of the phenomena. |
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| キーワード | （和） | 流行過程 / 感染症の蔓延 / 情報伝播 / SIRモデル / 動的モード分解 / / / |
| （英） | Epidemic Process / Infectious Disease Spread / Information Dissemination / SIR Model / Dynamic Mode Decomposition / / / | |
| 文献情報 | 信学技報, vol. 125, no. 222, IA2025-43, pp. 33-33, 2025年11月. | |
| 資料番号 | IA2025-43 | |
| 発行日 | 2025-10-28 (IA) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| 著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します．(許諾番号：10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| PDFダウンロード | IA2025-43 |
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| 研究会情報 | |
| 研究会 | IA |
| 開催期間 | 2025-11-04 - 2025-11-04 |
| 開催地（和） | Kantary Hills Hotel,Thailand |
| 開催地（英） | Kantary Hills Hotel,Thailand |
| テーマ（和） | IA2025 - Workshop on Internet Architecture and Applications 2025 |
| テーマ（英） | IA2025 - Workshop on Internet Architecture and Applications 2025 |
| 講演論文情報の詳細 | |
| 申込み研究会 | IA |
| 会議コード | 2025-11-IA |
| 本文の言語 | 英語 |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | A Study on a Method for Analyzing Epidemic Processes on Networks Using Dynamic Mode Decomposition |
| サブタイトル（英） | |
| キーワード(1)（和/英） | 流行過程 / Epidemic Process |
| キーワード(2)（和/英） | 感染症の蔓延 / Infectious Disease Spread |
| キーワード(3)（和/英） | 情報伝播 / Information Dissemination |
| キーワード(4)（和/英） | SIRモデル / SIR Model |
| キーワード(5)（和/英） | 動的モード分解 / Dynamic Mode Decomposition |
| キーワード(6)（和/英） | / |
| キーワード(7)（和/英） | / |
| キーワード(8)（和/英） | / |
| 第1著者 氏名（和/英/ヨミ） | 西岡 優太 / Yuta Nishioka / ニシオカ ユウタ |
| 第1著者 所属（和/英） | 関西学院大学 (略称： 関西学院大) Kwansei Gakuin University (略称： Kwansei Gakuin Univ.) |
| 第2著者 氏名（和/英/ヨミ） | 米田 千咲 / Chisaki Yoneda / ヨネダ チサキ |
| 第2著者 所属（和/英） | 関西学院大学 (略称： 関西学院大) Kwansei Gakuin University (略称： Kwansei Gakuin Univ.) |
| 第3著者 氏名（和/英/ヨミ） | 作元 雄輔 / Yusuke Sakumoto / サクモト ユウスケ |
| 第3著者 所属（和/英） | 関西学院大学 (略称： 関西学院大) Kwansei Gakuin University (略称： Kwansei Gakuin Univ.) |
| 講演者 | 第1著者 |
| 発表日時 | 2025-11-04 13:50:00 |
| 発表時間 | 45分 |
| 申込先研究会 | IA |
| 資料番号 | IA2025-43 |
| 巻番号（vol） | vol.125 |
| 号番号（no） | no.222 |
| ページ範囲 | p.33 |
| ページ数 | 1 |
| 発行日 | 2025-10-28 (IA) |
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