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Books > Business & Economics > Business & management > Management & management techniques > Operational research

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Epidemic Analytics for Decision Supports in COVID19 Crisis (Hardcover, 1st ed. 2022) Loot Price: R4,433
Discovery Miles 44 330
Epidemic Analytics for Decision Supports in COVID19 Crisis (Hardcover, 1st ed. 2022): Joao Alexandre Lobo Marques, Simon James...

Epidemic Analytics for Decision Supports in COVID19 Crisis (Hardcover, 1st ed. 2022)

Joao Alexandre Lobo Marques, Simon James Fong

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Loot Price R4,433 Discovery Miles 44 330 | Repayment Terms: R415 pm x 12*

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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.

General

Imprint: Springer Nature Switzerland AG
Country of origin: Switzerland
Release date: May 2022
First published: 2022
Editors: Joao Alexandre Lobo Marques • Simon James Fong
Dimensions: 235 x 155mm (L x W)
Format: Hardcover
Pages: 158
Edition: 1st ed. 2022
ISBN-13: 978-3-03-095280-8
Categories: Books > Professional & Technical > Technology: general issues > Engineering: general
Books > Business & Economics > Business & management > Management & management techniques > Operational research
Books > Medicine > General issues > Public health & preventive medicine > Epidemiology & medical statistics
Books > Computing & IT > Applications of computing > Databases > Data mining
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LSN: 3-03-095280-0
Barcode: 9783030952808

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