| 書目名稱 | Predictive Models for Decision Support in the COVID-19 Crisis |
| 編輯 | Joao Alexandre Lobo Marques,Francisco Nauber Berna |
| 視頻video | http://file.papertrans.cn/755/754604/754604.mp4 |
| 概述 | Showcases how artificial intelligence has been used to support the fight against COVID-19.Presents the benefits and limitations of the predictive models implemented during the epidemic.Advises on how |
| 叢書名稱 | SpringerBriefs in Applied Sciences and Technology |
| 圖書封面 |  |
| 描述 | 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.. |
| 出版日期 | Book 2021 |
| 關(guān)鍵詞 | Predictive Models; Decision Support; COVID-19 Crisis; Epidemiologic Models; Nonlinear Filtering; Artifici |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-61913-8 |
| isbn_softcover | 978-3-030-61912-1 |
| isbn_ebook | 978-3-030-61913-8Series ISSN 2191-530X Series E-ISSN 2191-5318 |
| issn_series | 2191-530X |
| copyright | The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 |
|Archiver|手機版|小黑屋|
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