Predictive models for decision support in the COVID-19 crisis / Joao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James, Fong.
2021
RA644.C67 M37 2021
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Details
Title
Predictive models for decision support in the COVID-19 crisis / Joao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James, Fong.
ISBN
9783030619138 (electronic book)
3030619133 (electronic book)
3030619125
9783030619121
3030619133 (electronic book)
3030619125
9783030619121
Published
Cham, Switzerland : Springer, [2021]
Language
English
Description
1 online resource (103 pages)
Other Standard Identifiers
10.1007/978-3-030-61913-8 doi
Call Number
RA644.C67 M37 2021
Dewey Decimal Classification
362.1962/414
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
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Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed February 16, 2021).
Series
SpringerBriefs in applied sciences and technology.
Available in Other Form
Print version: 9783030619121
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Online Access
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Table of Contents
Prediction for Decision Support During the COVID-19 Pandemic
Epidemiology Compartmental Models-SIR, SEIR, and SEIR with Intervention
Forecasting COVID-19 Time Series Based on an Autoregressive Model
Nonlinear Prediction for the COVID-19 Data Based on Quadratic Kalman Filtering
Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML
Predicting the Geographic Spread of the COVID-19 Pandemic: A Case Study from Brazil.
Epidemiology Compartmental Models-SIR, SEIR, and SEIR with Intervention
Forecasting COVID-19 Time Series Based on an Autoregressive Model
Nonlinear Prediction for the COVID-19 Data Based on Quadratic Kalman Filtering
Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML
Predicting the Geographic Spread of the COVID-19 Pandemic: A Case Study from Brazil.