Synthesis Lectures on Learning, Networks, and Algorithms

Hughes, James; Houghten, Sheridan; Dubé, Michael; Ashlock, Daniel; Brown, Joseph Alexander; Ashlock, Wendy; Stoodley, Matthew

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. 

Details

Published by: Springer

Publication Date: 2024-09-25

Format: Hardcover

ISBN-13: 9783031643729

DOI: 10.1007/978-3-031-64373-6

Dimensions: 240cm x168cm

Pages: 97