Predictive Models for Decision Support in the COVID-19 Crisis
(Sprache: Englisch)
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...
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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.
Inhaltsverzeichnis zu „Predictive Models for Decision Support in the COVID-19 Crisis “
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.
Autoren-Porträt von Joao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James Fong
Professor João Alexandre Lobo Marques gained his master's degree in 2007 and his PhD in 2009, both from the Federal University of Ceará, UFC, Brasil. He works as an associate professor at the University of Saint Joseph, Macau, and as a visiting associate professor at the Chinese Academy of Sciences. He is the CEO and co-founder of the XS Innovation Group in Brazil, which is focused on bioengineering innovation for education. He has published over 60 journal and conference papers, and has co-authored three books. His research interests include computational and artificial intelligence, data sciences, and neuroeconomics.
Bibliographische Angaben
- Autoren: Joao Alexandre Lobo Marques , Francisco Nauber Bernardo Gois , José Xavier-Neto , Simon James Fong
- 2020, 1st ed. 2021, VII, 98 Seiten, 41 farbige Abbildungen, Masse: 15,6 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3030619125
- ISBN-13: 9783030619121
Sprache:
Englisch
Pressezitat
"This book is ... of great interest for mathematical modelers--it nicely summarizes many important tools, with concrete examples, that could be adapted for other situations. ... I strongly recommend this book to advanced undergraduate engineers and mathematicians as well as specialists dealing with dynamical system modeling." (Arturo Ortiz-Tapia, Computing Reviews, July 26, 2022)
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