This textbook provides an exciting new addition to the area of network
science featuring a stronger and more methodical link of models to their
mathematical origin and explains how these relate to each other with
special focus on epidemic spread on networks. The content of the book is
at the interface of graph theory, stochastic processes and dynamical
systems. The authors set out to make a significant contribution to
closing the gap between model development and the supporting
mathematics. This is done by:
- Summarising and presenting the state-of-the-art in modeling epidemics
on networks with results and readily usable models signposted
throughout the book;
- Presenting different mathematical approaches to formulate exact and
solvable models;
- Identifying the concrete links between approximate models and their
rigorous mathematical representation;
- Presenting a model hierarchy and clearly highlighting the links
between model assumptions and model complexity;
- Providing a reference source for advanced undergraduate students, as
well as doctoral students, postdoctoral researchers and academic
experts who are engaged in modeling stochastic processes on
networks;
- Providing software that can solve differential equation models or
directly simulate epidemics on networks.
Replete with numerous diagrams, examples, instructive exercises, and
online access to simulation algorithms and readily usable code, this
book will appeal to a wide spectrum of readers from different
backgrounds and academic levels. Appropriate for students with or
without a strong background in mathematics, this textbook can form the
basis of an advanced undergraduate or graduate course in both
mathematics and other departments alike.
