Epidemic analytics for decision supports in COVID19 crisis / Joao Alexandre Lobo Marques, Simon James Fong, editors.
Marques, Joao Alexandre Lobo, editor.; Fong, Simon, editor.
2022
RA652
Available Online

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Title Epidemic analytics for decision supports in COVID19 crisis / Joao Alexandre Lobo Marques, Simon James Fong, editors.
ISBN 9783030952815 (electronic bk.)
3030952819 (electronic bk.)
9783030952808
3030952800
DOI https://doi.org/10.1007/978-3-030-95281-5
Published Cham : Springer, [2022]
Copyright ©2022
Language English
Description 1 online resource (vi, 158 pages) : illustrations (chiefly color)
Item Number 10.1007/978-3-030-95281-5 doi
Call Number RA652
Dewey Decimal Classification 614.402/85
Summary 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.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed May 26, 2022).
Added Author Marques, Joao Alexandre Lobo, editor.
Fong, Simon, editor.
Available in Other Form Print version: 9783030952808
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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.

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