8 research outputs found
The Effects of Evolutionary Adaptations on Spreading Processes in Complex Networks
A common theme among the proposed models for network epidemics is the
assumption that the propagating object, i.e., a virus or a piece of
information, is transferred across the nodes without going through any
modification or evolution. However, in real-life spreading processes, pathogens
often evolve in response to changing environments and medical interventions and
information is often modified by individuals before being forwarded. In this
paper, we investigate the evolution of spreading processes on complex networks
with the aim of i) revealing the role of evolution on the threshold,
probability, and final size of epidemics; and ii) exploring the interplay
between the structural properties of the network and the dynamics of evolution.
In particular, we develop a mathematical theory that accurately predicts the
epidemic threshold and the expected epidemic size as functions of the
characteristics of the spreading process, the evolutionary dynamics of the
pathogen, and the structure of the underlying contact network. In addition to
the mathematical theory, we perform extensive simulations on random and
real-world contact networks to verify our theory and reveal the significant
shortcomings of the classical mathematical models that do not capture
evolution. Our results reveal that the classical, single-type bond-percolation
models may accurately predict the threshold and final size of epidemics, but
their predictions on the probability of emergence are inaccurate on both random
and real-world networks. This inaccuracy sheds the light on a fundamental
disconnect between the classical bond-percolation models and real-life
spreading processes that entail evolution. Finally, we consider the case when
co-infection is possible and show that co-infection could lead the order of
phase transition to change from second-order to first-order.Comment: Submitte
Spreading processes in Multilayer Networks
Several systems can be modeled as sets of interconnected networks or networks
with multiple types of connections, here generally called multilayer networks.
Spreading processes such as information propagation among users of an online
social networks, or the diffusion of pathogens among individuals through their
contact network, are fundamental phenomena occurring in these networks.
However, while information diffusion in single networks has received
considerable attention from various disciplines for over a decade, spreading
processes in multilayer networks is still a young research area presenting many
challenging research issues. In this paper we review the main models, results
and applications of multilayer spreading processes and discuss some promising
research directions.Comment: 21 pages, 3 figures, 4 table
マルチレイヤーネットワークに基づくネットワーク生成モデル
学位の種別: 課程博士審査委員会委員 : (主査)東京大学准教授 鳥海 不二夫, 東京大学教授 大橋 弘忠, 東京大学教授 和泉 潔, 東京大学教授 青山 和浩, 東京大学准教授 陳 昱University of Tokyo(東京大学
Statistical physics of vaccination
Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination–one of the most important preventive measures of modern times–is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research