Computational Epidemiology: From Disease Transmission Modeling to Vaccination Decision Making
Sep 2020 · Springer Nature
Ebook
113
Pages
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About this ebook
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.
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.
About the author
Prof. Jiming Liu has been Chair Professor in Computer Science at Hong Kong Baptist University since 2010, where he also directs Centre for Health Informatics, as well as Joint Research Laboratory for Intelligent Disease Surveillance and Control (a research partnership with Chinese Center for Disease Control and Prevention). He received his MEng and PhD in Electrical Engineering (Robotics) from McGill University, after earning a BSc in Physics from East China Normal University and an interdisciplinary Master of Arts from Concordia University. He has been a Fellow of the IEEE since 2011. Prof. Liu’s research interests include AI for Social Good, Machine Learning, Complex Systems, Data-Driven Modeling, Complex Networks, Web Intelligence (WI), and Autonomy-Oriented Computing (AOC) Paradigms. He has served as Editor-in-Chief of Web Intelligence Journal, and Associate Editor of Big Data and Information Analytics, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Cybernetics, and Computational Intelligence, among a dozen of others.Dr. Shang Xia is currently an Associate Professor at the National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention (NIPD, China CDC), which is also recognized as the Chinese Center for Tropical Diseases Research (CTDR), and WHO Collaborating Centre for Tropical Diseases. He also works as a member of Joint Research Laboratory for Intelligent Disease Surveillance and Control (a research partnership with Hong Kong Baptist University). He received his PhD degree in Computer Science from Hong Kong Baptist University (HKBU) in Hong Kong, having obtained MEng and BEng degrees from Shanghai Jiao Tong University (SJTU), China. He has completed his Postdoctoral Research Program in the Chinese Center for Diseases Control and Prevention (China CDC) in Beijing, China. He is the committee member of International Society of Geospatial Health (GnosisGIS). He has served as the section editors of BMC Infectious Diseases of Poverty (IDP), and Climate Change Research (in Chinese).
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