AI Versus Epidemics

2024 ed.

by Daniel Ashlock , James Hughes , Joseph Alexander Brown , Matthew Stoodley , Michael Dube , Sheridan Houghten , Wendy Ashlock

Book Details

Format Hardback or Cased Book

Book Series Synthesis Lectures on Learning, Networks, and Algorithms

ISBN-10 3031643720

ISBN-13 9783031643729

Edition 2024 ed.

Publisher Springer International Publishing AG

Imprint Springer International Publishing AG

Country of Manufacture GB

Country of Publication GB

Publication Date Sep 25th, 2024

Print length 97 Pages

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Ksh 6,300.00

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This book presents algorithms and tools that are designed to model and extract information from personal contact networks, which represent which individuals in a population are physically in contact with one another. The authors developed these tools based on research they conducted during the COVID-19 pandemic, with the goal of improving responses to epidemics in the future. The book provides methods for modelling the transmission of infection across a population. The authors explain how an epidemic model can be used to strategically distribute vaccines and minimize the spread of a virus. The book shows how evolutionary computation, graph compression, and network induction can be utilized to manage issues that arise from an epidemic.

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