Computational Epidemiology: From Disease Transmission Modeling to Vaccination Decision Making

ISBN-13: 9783030521073 / Angielski / Twarda / 2020 / 113 str.

Jiming Liu; Xia Shang

Recenzje

“This is a timely overview of computational modeling of infectious disease transmission and vaccination strategies. ... The book targets public health and computational researchers or graduate students newly interested in questions of infectious disease and vaccination modeling.” (Jacob E Simmering, Doody's Book Reviews, January 16, 2021)

Spis treści

1 Paradigms in Epidemiology

1.1 Methodological Paradigms

1.2 Recent Developments

1.3 Infectious Diseases and Vaccination 

1.4 Objectives and Tasks 

1.4.1 Modeling Infectious Disease Dynamics 

1.4.2 Modeling Vaccine Allocation Strategies 

1.4.3 Modeling Vaccination Decision-Making 

1.4.4 Modeling Subjective Perception 

1.5 Summary 

2 Computational Modeling in a Nutshell

2.1 Modeling Infectious Disease Dynamics 

2.1.1 Infectious Disease Models 

2.1.2 Age-Specific Disease Transmissions

2.2 Modeling Contact Relationships 

2.2.1 Empirical Methods 

2.2.2 Computational Methods

2.3 Case Study 

2.3.1 2009 Hong Kong H1N1 Influenza Epidemic 

2.3.2 Age-Specific Contact Matrices 

2.3.3 Validation

2.4 Further Remarks 

2.5 Summary

3 Strategizing Vaccine Allocation

3.1 Vaccination

3.1.1 Herd Immunity 

3.1.2 Vaccine Allocation Strategy

3.2 Vaccination Priorities 

3.3 Age-Specific Intervention Priorities 

3.3.1 Modeling Prioritized Interventions 

3.3.2 Effects of Vaccination 

3.3.3 Effects of Contact Reduction

3.3.4 Integrated Measures 

3.4 Case Study 

3.4.1 2009 Hong Kong HSI Vaccination Programme 

3.4.2 Effects of Prioritized Interventions

3.5 Further Remarks

3.6 Summary

4 Explaining Individuals’ Vaccination Decisions

4.1 Costs and Benefits for Decision-Making

4.2 Game-Theoretic Modeling of Vaccination Decision-Making

4.3 Case Study 

4.3.1 2009 Hong Kong HSI Vaccination Programme

4.3.2 Vaccination Coverage 

4.4 Further Remarks

4.5 Summary 

5 Characterizing Socially Influenced Vaccination Decisions 

5.1 Social Influences on Vaccination Decision-Making 

5.2 Case Study 

5.2.1 Vaccination Coverage 

5.3 Further Remarks

5.4 Summary 

6 Understanding the Effect of Social Media 

6.1 Modeling Subjective Perception 

6.2 Subjective Perception in Vaccination Decision-Making 

6.2.1 Dempster-Shafer Theory (DST)

6.2.2 Spread of Social Awareness 

6.3 Case Study 

6.3.1 Vaccination Decision-Making in an Online Social

Community

6.3.2 Interplay of Two Dynamics 

6.4 Further Remarks 

6.5 Summary

7 Welcome to the Era of Systems Epidemiology 

7.1 Systems Thinking in Epidemiology 

7.2 Systems Modeling in Principle

7.3 Systems Modeling in Practice

7.4 Toward Systems Epidemiology 

8 Further Readings 

References 

O autorze

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). 

Blurb

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.