This book covers recent developments in epidemic process models and related data on temporally varying networks. It is widely recognized that contact networks are indispensable for describing, understanding, and intervening to stop the spread of infectious diseases in human and animal populations; network epidemiology is an umbrella term to describe this research field.
More recently, contact networks have been recognized as being highly dynamic. This observation, also supported by an increasing amount of new data, has led to research on temporal networks, a rapidly growing area. Changes in network structure are often informed by epidemic (or other) dynamics, in which case they are referred to as adaptive networks.
This volume gathers contributions by prominent authors working in temporal and adaptive network epidemiology, a field essential to understanding infectious diseases in real society.
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1 Introduction to Temporal Network Epidemiology |
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1 | (16) |
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2 How Behaviour and the Environment Influence Transmission in Mobile Groups |
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17 | (26) |
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3 Sensitivity to Temporal and Topological Misinformation in Predictions of Epidemic Outbreaks |
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43 | (14) |
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4 Measuring Propagation with Temporal Webs |
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57 | (48) |
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5 Mean Field at Distance One |
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105 | (24) |
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6 Towards Identifying and Predicting Spatial Epidemics on Complex Meta-population Networks |
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129 | (32) |
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7 Epidemic Threshold in Temporally-Switching Networks |
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161 | (18) |
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8 Control Strategies of Contagion Processes in Time-Varying Networks |
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179 | (20) |
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9 Leveraging Topological and Temporal Structure of Hospital Referral Networks for Epidemic Control |
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199 | (16) |
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10 Surveillance for Outbreak Detection in Livestock-Trade Networks |
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215 | (26) |
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11 Optimal Containment of Epidemics in Temporal and Adaptive Networks |
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241 | (26) |
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12 Mapping Out Emerging Network Structures in Dynamic Network Models Coupled with Epidemics |
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267 | (24) |
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13 Disease Spreading in Time-Evolving Networked Communities |
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291 | (26) |
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14 Toward a Realistic Modeling of Epidemic Spreading with Activity Driven Networks |
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317 | |
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Naoki Masuda has been a Senior Lecturer at the University of Bristols Department of Engineering Mathematics since 2014. His research interests include both theoretical and data-science aspects of network science, computational neuroscience including neuroinformatics, behavioural ecology, and mathematical biology in general. He received his PhD from the University of Tokyo in 1998. After postdocs in Japan, he worked as a Lecturer and Associate Professor at the University of Tokyo between 2006 and 2014, prior to joining the University of Bristol.
Petter Holme is a specially appointed professor at the Institute of Innovative Research, Tokyo Institute of Technology, Japan. His research is both data driven and theoretical and covers many aspects of large-scale structures of natural and social systems. Lately he has also become interested in how to integrate information about time with network methods. Holme has a PhD in theoretical physics from Umeå University, Sweden. After postdocs at the Department of Physics, University of Michigan, and Department of Computer Science, University of New Mexico, he served as an Assistant Professor at the School for Computer Science and Communication, Royal Institute of Technology, Sweden; the Department of Physics, Umeå University, Sweden; and Sungkyunkwan University, Korea before joining Tokyo Institute of Technology.
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