Predictive Models for Decision Support in the COVID-19 Crisis
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Beschreibung
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.
Showcases how artificial intelligence has been used to support the fight against COVID-19 Presents the benefits and limitations of the predictive models implemented during the epidemic Advises on how these tools may be better used in the future
Autor*in
Joao Alexandre Lobo Marques
Themen in »Predictive Models for Decision Support in the COVID-19 Crisis«
Predictive Models Decision Support COVID-19 Crisis Epidemiologic Models Nonlinear Filtering Artificial Intelligence Model Disease Tracking Disease Prediction Pandemic Modeling Coronavirus
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“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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