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AI Versus Epidemics. 2024 ed.
・ISBN 978-3-031-64372-9 hard EUR 39.99
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★★★
| 著者・編者 | Hughes, James / Houghten, Sheridan / Dube, Michael / Ashlock, Daniel / Brown, Joseph Alexander / Ashlock, Wendy / Stoodley, Matthew, |
|---|---|
| シリーズ | (Synthesis Lectures on Learning, Networks, and Algorithms) |
| 出版社 | (Springer International Publishing AG, SZ) |
| 出版年月 | 2024 |
| ページ数 | 97 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A03-46195＞ |
解説
This book presents algorithms and tools that are designed to model and extract information from personal contact networks, which represent which individuals in a population are physically in contact with one another. The authors developed these tools based on research they conducted during the COVID-19 pandemic, with the goal of improving responses to epidemics in the future. The book provides methods for modelling the transmission of infection across a population. The authors explain how an epidemic model can be used to strategically distribute vaccines and minimize the spread of a virus. The book shows how evolutionary computation, graph compression, and network induction can be utilized to manage issues that arise from an epidemic.