Predictive Models for Decision Support in the COVID-19 Crisis [electronic resource] /
by Joao Alexandre Lobo Marques, Francisco Nauber Bernardo Gois, José Xavier-Neto, Simon James Fong.
- 1st ed. 2021.
- VII, 98 p. 48 illus., 41 illus. in color. online resource.
- SpringerBriefs in Applied Sciences and Technology, 2191-5318 .
- SpringerBriefs in Applied Sciences and Technology, .
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.
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.
9783030619138
10.1007/978-3-030-61913-8 doi
Industrial Management. Epidemiology. Operations research. Data mining. Medicine, Preventive. Health promotion. Industrial Management. Epidemiology. Operations Research and Decision Theory. Data Mining and Knowledge Discovery. Health Promotion and Disease Prevention.