Predictive Models for Decision Support in the COVID-19 Crisis
Simon James Fong, José Xavier-Neto, Francisco Nauber Bernardo Gois, et al.
Springer International Publishing 
Naturwissenschaften, Medizin, Informatik, Technik / Sonstiges
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.
Kundenbewertungen
Disease Tracking, Epidemiologic Models, Coronavirus, Decision Support, Predictive Models, Disease Prediction, Artificial Intelligence Model, Pandemic Modeling, Nonlinear Filtering, COVID-19 Crisis