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Predictive Models for Decision Support in the COVID-19 Crisis. 1st ed. 2021
・ISBN 978-3-030-61912-1 paper EUR 59.99
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| 著者・編者 | Marques, Joao Alexandre Lobo / Gois, Francisco Nauber Bernardo / Xavier-Neto, Jose / Fong, Simon James, |
|---|---|
| シリーズ | (SpringerBriefs in Applied Sciences and Technology) |
| 出版社 | (Springer Nature Switzerland AG, SZ) |
| 出版年月 | 2020 |
| ページ数 | 98 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A02-54024＞ |
解説
COVID-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting the virus, enormously tap into the power of artificial intelligence and its predictive models for urgent decision support. This book showcases a collection of important predictive models that used during the pandemic, and discusses and compares their efficacy and limitations.
Readers from both healthcare industries and academia can gain unique insights on how predictive models were designed and applied on epidemic data. Taking COVID19 as a case study and showcasing the lessons learnt, this book will enable readers to be better prepared in the event of virus epidemics or pandemics in the future.