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.

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

Autorentext

James Alexander Hughes, Ph.D., is a Professor in the Department of Computer Science at St. Francis Xavier University. He received his Ph.D. from the University of Western Ontario. His research interests include machine learning, artificial intelligence, evolutionary computation, artificial neural networks, mathematical modelling, brain connectivity, and other real world applications.Sheridan Houghten, Ph.D., is a Professor in the Department of Computer Science at Brock University. She received her Ph.D. from Concordia University. Her research interests include combinatorial optimization, computational intelligence, and algorithms, with various application areas including bioinformatics, graphs, and coding theory.Michael Dubé is a Ph.D. student at the University of Guelph. He earned his Master's degree from Brock University. His thesis investigated epidemic modeling, simulation, and deployment of vaccination strategies on personal contact networks using evolutionary computation.Matthew Stoodley, Ph.D., is a Senior Bioinformatics Analyst at the University Health Network in Toronto. He received his Ph.D. from the University of Guelph. His interest lies in designing effective solutions for complex problems using computational analysis of large biological datasets. Daniel Ashlock, Ph.D., was the Chair of the Department of Mathematics and Statistics at the University of Guelph. He authored over 300 articles and several books. His primary research areas were evolutionary computation, bioinformatics, mathematical biology, and graph theory, Joseph Alexander Brown, Ph.D., is an Assistant Teaching Professor at Thompson Rivers University. He earned his Ph.D. from the University of Guelph. His research interests include evolutionary computation, computational creativity, computational intelligence, game design, game theory, and bioinformatics. Wendy Ashlock, Ph.D., is the Chief Data Scientist at Ashlock and McGuinness Consulting, Inc. She earned her Ph.D. from York University. She is an expert in applying computational intelligence and machine learning to bioinformatics.

Inhalt

Chapter 1 Introduction.- Chapter 2 Evolutionary Computation.- Chapter 3 Graph Compression.- Chapter 4 Network Induction.