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
