04756nam a22006735i 4500 978-3-031-64373-6 DE-He213 20251030113103.0 cr nn 008mamaa 240925s2024 sz | s |||| 0|eng d 9783031643736 978-3-031-64373-6 10.1007/978-3-031-64373-6 doi Q334-342 TA347.A78 UYQ bicssc COM004000 bisacsh UYQ thema Hughes, James. author. aut http://id.loc.gov/vocabulary/relators/aut 84946 AI Versus Epidemics [electronic resource] / by James Hughes, Sheridan Houghten, Michael Dubé, Daniel Ashlock, Joseph Alexander Brown, Wendy Ashlock, Matthew Stoodley. 1st ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XI, 97 p. 57 illus., 35 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Synthesis Lectures on Learning, Networks, and Algorithms, 2690-4314 Chapter 1 Introduction -- Chapter 2 Evolutionary Computation -- Chapter 3 Graph Compression -- Chapter 4 Network Induction. This book presents algorithms and tools that are designed to model and extract information from personal contact networks that 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. In addition, this book: Demonstrates applied techniques for researchers and professionals working on solving problems related to epidemics Explains why personal contact networks are the key to understanding the dynamics of an epidemic managing related issues Provides solutions to problems that occur when creating and utilizing models of large populations About the Authors: James Alexander Hughes, Ph.D., is a Professor in the Department of Computer Science at St. Francis Xavier University. Sheridan Houghten, Ph.D., is a Professor in the Department of Computer Science at Brock University. Michael Dubé is a Ph.D. student at the University of Guelph. He earned his Master’s degree from Brock University. Matthew Stoodley, Ph.D., is a Senior Bioinformatics Analyst at the University Health Network in Toronto. Daniel Ashlock, Ph.D., was the Chair of the Department of Mathematics and Statistics at the University of Guelph. Joseph Alexander Brown, Ph.D., is an Assistant Teaching Professor at Thompson Rivers University. Artificial intelligence. 58441 Machine learning. 55059 Medical sciences. 65984 Public health. 61312 Application software. Algorithms. 58677 Artificial Intelligence. 58441 Machine Learning. 55059 Health Sciences. 65987 Public Health. 61312 Computer and Information Systems Applications. Algorithms. 58677 Houghten, Sheridan. author. aut http://id.loc.gov/vocabulary/relators/aut 84947 Dubé, Michael. author. aut http://id.loc.gov/vocabulary/relators/aut 84948 Ashlock, Daniel. author. aut http://id.loc.gov/vocabulary/relators/aut 84949 Brown, Joseph Alexander. author. aut http://id.loc.gov/vocabulary/relators/aut 84950 Ashlock, Wendy. author. aut http://id.loc.gov/vocabulary/relators/aut 84951 Stoodley, Matthew. author. aut http://id.loc.gov/vocabulary/relators/aut 84952 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031643729 Printed edition: 9783031643743 Printed edition: 9783031643750 Synthesis Lectures on Learning, Networks, and Algorithms, 2690-4314 84953 https://doi.org/10.1007/978-3-031-64373-6 ZDB-2-SXSC 63681 63681