This book provides a comprehensive introduction to computational
epidemiology, highlighting its major methodological paradigms throughout
the development of the field while emphasizing the needs for a new
paradigm shift in order to most effectively address the increasingly
complex real-world challenges in disease control and prevention.
Specifically, the book presents the basic concepts, related
computational models, and tools that are useful for characterizing
disease transmission dynamics with respect to a heterogeneous host
population. In addition, it shows how to develop and apply computational
methods to tackle the challenges involved in population-level
intervention, such as prioritized vaccine allocation. A unique feature
of this book is that its examination on the issues of vaccination
decision-making is not confined only to the question of how to develop
strategic policies on prioritized interventions, as it further
approaches the issues from the perspective of individuals, offering a
well integrated cost-benefit and social-influence account for voluntary
vaccination decisions. One of the most important contributions of this
book lies in it offers a blueprint on a novel methodological paradigm in
epidemiology, namely, systems epidemiology, with detailed systems
modeling principles, as well as practical steps and real-world examples,
which can readily be applied in addressing future systems
epidemiological challenges.
The book is intended to serve as a reference book for researchers and
practitioners in the fields of computer science and epidemiology.
Together with the provided references on the key concepts, methods, and
examples being introduced, the book can also readily be adopted as an
introductory text for undergraduate and graduate courses in
computational epidemiology as well as systems epidemiology, and as
training materials for practitioners and field workers.
