國立虎尾科技大學 |

Epidemic Analytics for Decision Supports in COVID19 Crisis

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Epidemic Analytics for Decision Supports in COVID19 Crisis/ edited by Joao Alexandre Lobo Marques, Simon James Fong.
other author:	Marques, Joao Alexandre Lobo.
Description:	VI, 158 p. 87 illus., 77 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Industrial Management. -
Online resource:	https://doi.org/10.1007/978-3-030-95281-5
ISBN:	9783030952815

Epidemic Analytics for Decision Supports in COVID19 Crisis
Epidemic Analytics for Decision Supports in COVID19 Crisis[electronic resource] /edited by Joao Alexandre Lobo Marques, Simon James Fong. - 1st ed. 2022. - VI, 158 p. 87 illus., 77 illus. in color.online resource.

Chapter 1. Research and Technology Development Achievements During the COVID-19 Pandemic – An Overview -- Chapter 2. Analysis of the COVID-19 Pandemic Behavior based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models -- Chapter 3. The Comparison of Different Linear and Nonlinear Models Using Preliminary Data to Efficiently Analyze the COVID-19 Outbreak -- Chapter 4. Probabilistic Forecasting Model for the COVID-19 Pandemic based on the Composite Monte Carlo Model Integrated with Deep Learning and Fuzzy System -- Chapter 5. The Application of Supervised and Unsupervised Computational Predictive Models to Simulate the COVID-19 Pandemic -- Chapter 6. A Quantum Field formulation for a pandemic propagation.

Covid-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting against the virus, enormously tap on the power of AI and its data analytics models for urgent decision supports at the greatest efforts, ever seen from human history. This book showcases a collection of important data analytics models that were used during the epidemic, and discusses and compares their efficacy and limitations. Readers who from both healthcare industries and academia can gain unique insights on how data analytics models were designed and applied on epidemic data. Taking Covid-19 as a case study, readers especially those who are working in similar fields, would be better prepared in case a new wave of virus epidemic may arise again in the near future.

ISBN: 9783030952815

Standard No.: 10.1007/978-3-030-95281-5doiSubjects--Topical Terms:

1366261
Industrial Management.

LC Class. No.: HD28-70

Dewey Class. No.: 658.5

Epidemic Analytics for Decision Supports in COVID19 Crisis
LDR:02982nam a22004215i 4500 001 1090185
003 DE-He213
005 20220520132440.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783030952815 $9 978-3-030-95281-5
024 7 $a 10.1007/978-3-030-95281-5 $2 doi
035 $a 978-3-030-95281-5
050 4 $a HD28-70
072 7 $a TBC $2 bicssc
072 7 $a KJM $2 bicssc
072 7 $a TEC000000 $2 bisacsh
072 7 $a TBC $2 thema
072 7 $a KJM $2 thema
082 0 4 $a 658.5 $2 23
245 1 0 $a Epidemic Analytics for Decision Supports in COVID19 Crisis $h [electronic resource] / $c edited by Joao Alexandre Lobo Marques, Simon James Fong.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a VI, 158 p. 87 illus., 77 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Chapter 1. Research and Technology Development Achievements During the COVID-19 Pandemic – An Overview -- Chapter 2. Analysis of the COVID-19 Pandemic Behavior based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models -- Chapter 3. The Comparison of Different Linear and Nonlinear Models Using Preliminary Data to Efficiently Analyze the COVID-19 Outbreak -- Chapter 4. Probabilistic Forecasting Model for the COVID-19 Pandemic based on the Composite Monte Carlo Model Integrated with Deep Learning and Fuzzy System -- Chapter 5. The Application of Supervised and Unsupervised Computational Predictive Models to Simulate the COVID-19 Pandemic -- Chapter 6. A Quantum Field formulation for a pandemic propagation.
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 against the virus, enormously tap on the power of AI and its data analytics models for urgent decision supports at the greatest efforts, ever seen from human history. This book showcases a collection of important data analytics models that were used during the epidemic, and discusses and compares their efficacy and limitations. Readers who from both healthcare industries and academia can gain unique insights on how data analytics models were designed and applied on epidemic data. Taking Covid-19 as a case study, readers especially those who are working in similar fields, would be better prepared in case a new wave of virus epidemic may arise again in the near future.
650 0 $a Industrial Management. $3 1366261
650 0 $a Epidemiology. $3 635923
650 0 $a Operations research. $3 573517
650 0 $a Data mining. $3 528622
650 0 $a Medicine, Preventive. $3 582813
650 0 $a Health promotion. $3 565089
650 2 4 $a Operations Research and Decision Theory. $3 1366301
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Health Promotion and Disease Prevention. $3 593964
700 1 $a Marques, Joao Alexandre Lobo. $e author. $0 (orcid)0000-0002-6472-8784 $1 https://orcid.org/0000-0002-6472-8784 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1361898
700 1 $a Fong, Simon James. $e author. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1349813
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030952808
776 0 8 $i Printed edition: $z 9783030952822
776 0 8 $i Printed edition: $z 9783030990213
856 4 0 $u https://doi.org/10.1007/978-3-030-95281-5
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)