Go to main content

Predictive models for decision support in the COVID-19 crisis / Joao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James, Fong.

Marques, Joao Alexandre Lobo, author.; Gois, Francisco Nauber Bernardo, author.; Xavier-Neto, José, author.; Fong, Simon, author.
2021
RA644.C67 M37 2021
Available Online
Formats
Format
BibTeX
MARCXML
TextMARC
MARC
DublinCore
EndNote
NLM
RefWorks
RIS
Add to Basket
Cite
Citation

Linked e-resources

Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks

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.
Author
Marques, Joao Alexandre Lobo, author.
ISBN
9783030619138 (electronic book)
3030619133 (electronic book)
3030619125
9783030619121
DOI
https://doi.org/10.1007/978-3-030-61913-8
Published
Cham, Switzerland : Springer, [2021]
Language
English
Description
1 online resource (103 pages).
Item Number
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.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed February 16, 2021).
Added Author
Gois, Francisco Nauber Bernardo, author.
Xavier-Neto, José, author.
Fong, Simon, author.
Series
SpringerBriefs in applied sciences and technology.
Available in Other Form
Print version: 9783030619121
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
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.

Browse Subjects

Show more subjects...

Statistics

from
to
Export