Predictive Models for Decision Support in the COVID-19 Crisis von Joao Alexandre Lobo Marques | ISBN 9783030619138

Predictive Models for Decision Support in the COVID-19 Crisis

von Joao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto und Simon James Fong
Mitwirkende
Autor / AutorinJoao Alexandre Lobo Marques
Autor / AutorinFrancisco Nauber Bernardo Gois
Autor / AutorinJosé Xavier-Neto
Autor / AutorinSimon James Fong
Buchcover Predictive Models for Decision Support in the COVID-19 Crisis | Joao Alexandre Lobo Marques | EAN 9783030619138 | ISBN 3-030-61913-3 | ISBN 978-3-030-61913-8
“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)

Predictive Models for Decision Support in the COVID-19 Crisis

von Joao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto und Simon James Fong
Mitwirkende
Autor / AutorinJoao Alexandre Lobo Marques
Autor / AutorinFrancisco Nauber Bernardo Gois
Autor / AutorinJosé Xavier-Neto
Autor / AutorinSimon James Fong

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.