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Epidemic analytics for decision supp...

Marques, Joao Alexandre Lobo.

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Epidemic analytics for decision supports in COVID19 crisis

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Epidemic analytics for decision supports in COVID19 crisis/ edited by Joao Alexandre Lobo Marques, Simon James Fong.
其他作者:	Marques, Joao Alexandre Lobo.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	vi, 158 p. :ill. (chiefly col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Epidemiology - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-030-95281-5
ISBN:	9783030952815

Epidemic analytics for decision supports in COVID19 crisis
Epidemic analytics for decision supports in COVID19 crisis[electronic resource] /edited by Joao Alexandre Lobo Marques, Simon James Fong. - Cham :Springer International Publishing :2022. - vi, 158 p. :ill. (chiefly col.), digital ;24 cm.

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.

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.

ISBN: 9783030952815

Standard No.: 10.1007/978-3-030-95281-5doiSubjects--Topical Terms:

3381052
Epidemiology
--Data processing.

LC Class. No.: RA652 / .E65 2022

Dewey Class. No.: 614.40285

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