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Predictive models for decision suppo...

Marques, Joao Alexandre Lobo.

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Predictive models for decision support in the COVID-19 crisis

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predictive models for decision support in the COVID-19 crisis/ by Joao Alexandre Lobo Marques ... [et al.].
other author:	Marques, Joao Alexandre Lobo.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	vii, 98 p. :ill., digital ;24 cm.
[NT 15003449]:	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
Subject:	COVID-19 (Disease) - Epidemiology. -
Online resource:	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

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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.
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