AI Versus Epidemics

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.