Description
Product ID: | 9783030619121 |
Product Form: | Paperback / softback |
Country of Manufacture: | GB |
Series: | SpringerBriefs in Applied Sciences and Technology |
Title: | Predictive Models for Decision Support in the COVID-19 Crisis |
Authors: | Author: Francisco Nauber Bernardo Gois, Simon James Fong, Jose Xavier-Neto, Joao Alexandre Lobo Marques |
Page Count: | 98 |
Subjects: | Management and management techniques, Management & management techniques, Management decision making, Operational research, Public health and preventive medicine, Epidemiology and Medical statistics, Engineering: general, Data mining, Expert systems / knowledge-based systems, Management decision making, Operational research, Public health & preventive medicine, Epidemiology & medical statistics, Engineering: general, Data mining, Expert systems / knowledge-based systems |
Description: | 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. 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. |
Imprint Name: | Springer Nature Switzerland AG |
Publisher Name: | Springer Nature Switzerland AG |
Country of Publication: | GB |
Publishing Date: | 2020-12-01 |