東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Economic policy- Decision making." ]

切換: 標籤 | MARC模式 | ISBD

Predictive models for decision suppo...

Marques, Joao Alexandre Lobo.

FindBook

Google Book

Amazon

博客來

Predictive models for decision support in the COVID-19 crisis

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Predictive models for decision support in the COVID-19 crisis/ by Joao Alexandre Lobo Marques ... [et al.].
其他作者:	Marques, Joao Alexandre Lobo.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	vii, 98 p. :ill., digital ;24 cm.
內容註:	Chapter 1. Prediction for Decision Support during the COVID-19 Pandemic -- Chapter 2. Epidemiology Compartmental Models - SIR, SEIR and SEIR with Intervention -- Chapter 3. Forecasting COVID-19 Time Series based on an Auto Regressive Model -- Chapter 4. Nonlinear Prediction for the COVID-19 Data based on Quadratic Kalman Filtering -- Chapter 5. Artiﬁcial Intelligence Prediction for the COVID-19 Data based on LSTM Neural Networks and H2O AutoML -- Chapter 6. Predicting the Geographic Spread of the COVID-19 Pandemic: a case study from Brazil.
Contained By:	Springer Nature eBook
標題:	COVID-19 (Disease) - Epidemiology. -
電子資源:	https://doi.org/10.1007/978-3-030-61913-8
ISBN:	9783030619138

Predictive models for decision support in the COVID-19 crisis
Predictive models for decision support in the COVID-19 crisis[electronic resource] /by Joao Alexandre Lobo Marques ... [et al.]. - Cham :Springer International Publishing :2021. - vii, 98 p. :ill., digital ;24 cm. - SpringerBriefs in applied sciences and technology,2191-530X. - SpringerBriefs in applied sciences and technology..

Chapter 1. Prediction for Decision Support during the COVID-19 Pandemic -- Chapter 2. Epidemiology Compartmental Models - SIR, SEIR and SEIR with Intervention -- Chapter 3. Forecasting COVID-19 Time Series based on an Auto Regressive Model -- Chapter 4. Nonlinear Prediction for the COVID-19 Data based on Quadratic Kalman Filtering -- Chapter 5. Artiﬁcial Intelligence Prediction for the COVID-19 Data based on LSTM Neural Networks and H2O AutoML -- Chapter 6. Predicting the Geographic Spread of the COVID-19 Pandemic: a case study from Brazil.

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.

ISBN: 9783030619138

Standard No.: 10.1007/978-3-030-61913-8doiSubjects--Topical Terms:

3487785
COVID-19 (Disease)
--Epidemiology.

LC Class. No.: RA644.C67

Dewey Class. No.: 362.1962414

Predictive models for decision support in the COVID-19 crisis
LDR:02407nmm a2200349 a 4500 001 2236410
003 DE-He213
005 20201130113623.0
006 m d
007 cr nn 008maaau
008 211111s2021 sz s 0 eng d
020 $a 9783030619138 $q (electronic bk.)
020 $a 9783030619121 $q (paper)
024 7 $a 10.1007/978-3-030-61913-8 $2 doi
035 $a 978-3-030-61913-8
040 $a GP $c GP $e rda
041 0 $a eng
050 4 $a RA644.C67
072 7 $a TBC $2 bicssc
072 7 $a TEC000000 $2 bisacsh
072 7 $a TBC $2 thema
072 7 $a KJMV $2 thema
082 0 4 $a 362.1962414 $2 23
090 $a RA644.C67 $b P923 2021
245 0 0 $a Predictive models for decision support in the COVID-19 crisis $h [electronic resource] / $c by Joao Alexandre Lobo Marques ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a vii, 98 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in applied sciences and technology, $x 2191-530X
505 0 $a Chapter 1. Prediction for Decision Support during the COVID-19 Pandemic -- Chapter 2. Epidemiology Compartmental Models - SIR, SEIR and SEIR with Intervention -- Chapter 3. Forecasting COVID-19 Time Series based on an Auto Regressive Model -- Chapter 4. Nonlinear Prediction for the COVID-19 Data based on Quadratic Kalman Filtering -- Chapter 5. Artiﬁcial Intelligence Prediction for the COVID-19 Data based on LSTM Neural Networks and H2O AutoML -- Chapter 6. Predicting the Geographic Spread of the COVID-19 Pandemic: a case study from Brazil.
520 $a 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.
650 0 $a COVID-19 (Disease) $x Epidemiology. $3 3487785
650 0 $a Epidemiology $x Statistical methods. $3 616294
650 0 $a Predictive analytics. $3 3487786
650 0 $a Medical policy $x Decision making. $3 3487787
650 0 $a Economic policy $x Decision making. $3 700025
650 1 4 $a Engineering Economics, Organization, Logistics, Marketing. $3 891023
650 2 4 $a Epidemiology. $3 568544
650 2 4 $a Operations Research/Decision Theory. $3 890895
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Health Promotion and Disease Prevention. $3 894308
700 1 $a Marques, Joao Alexandre Lobo. $3 3487784
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in applied sciences and technology. $3 1565541
856 4 0 $u https://doi.org/10.1007/978-3-030-61913-8
950 $a Engineering (SpringerNature-11647)