TY  - BOOK
AU  - Marques,Joao Alexandre Lobo
AU  - Fong,Simon James
ED  - SpringerLink (Online service)
TI  - Epidemic Analytics for Decision Supports in COVID19 Crisis
SN  - 9783030952815
PY  - 2022///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Industrial Management
KW  - Epidemiology
KW  - Operations research
KW  - Data mining
KW  - Medicine, Preventive
KW  - Health promotion
KW  - Operations Research and Decision Theory
KW  - Data Mining and Knowledge Discovery
KW  - Health Promotion and Disease Prevention
KW  - COVID-19 -- epidemiology
KW  - Decision Support Systems, Clinical
KW  - Decision Support Techniques
KW  - Electronic Data Processing
KW  - Epidemiologic Methods
N1  - Chapter 1. Research and Technology Development Achievements During the COVID-19 Pandemic – An Overview -- Chapter 2. Analysis of the COVID-19 Pandemic Behavior based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models -- Chapter 3. The Comparison of Different Linear and Nonlinear Models Using Preliminary Data to Efficiently Analyze the COVID-19 Outbreak -- Chapter 4. Probabilistic Forecasting Model for the COVID-19 Pandemic based on the Composite Monte Carlo Model Integrated with Deep Learning and Fuzzy System -- Chapter 5. The Application of Supervised and Unsupervised Computational Predictive Models to Simulate the COVID-19 Pandemic -- Chapter 6. A Quantum Field formulation for a pandemic propagation
N2  - Covid-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting against the virus, enormously tap on the power of AI and its data analytics models for urgent decision supports at the greatest efforts, ever seen from human history. This book showcases a collection of important data analytics models that were used during the epidemic, and discusses and compares their efficacy and limitations. Readers who from both healthcare industries and academia can gain unique insights on how data analytics models were designed and applied on epidemic data. Taking Covid-19 as a case study, readers especially those who are working in similar fields, would be better prepared in case a new wave of virus epidemic may arise again in the near future
UR  - https://doi.org/10.1007/978-3-030-95281-5
ER  -