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To understand the contact patterns of a population -- who is in contact with whom, and when the contacts happen -- is crucial for modeling outbreaks of infectious disease. Traditional theoretical epidemiology assumes that any individual can…

种群与进化 · 定量生物学 2015-10-22 Petter Holme

Understanding how epidemics spread in a system is a crucial step to prevent and control outbreaks, with broad implications on the system's functioning, health, and associated costs. This can be achieved by identifying the elements at higher…

物理与社会 · 物理学 2015-03-16 Eugenio Valdano , Chiara Poletto , Armando Giovannini , Diana Palma , Lara Savini , Vittoria Colizza

We study spreading on networks where the contact dynamics between the nodes is governed by a random process and where the inter-contact time distribution may differ from the exponential. We consider a process of imperfect spreading, where…

物理与社会 · 物理学 2016-07-07 Martin Gueuning , Jean-Charles Delvenne , Renaud Lambiotte

Epidemic outbreaks of new pathogens, or known pathogens in new populations, cause a great deal of fear because they are hard to predict. For theoretical models of disease spreading, on the other hand, quantities characterizing the outbreak…

种群与进化 · 定量生物学 2015-05-20 Petter Holme , Taro Takaguchi

We investigate disease spreading on eight empirical data sets of human contacts (mostly proximity networks recording who is close to whom, at what time). We compare three levels of representations of these data sets: temporal networks,…

种群与进化 · 定量生物学 2016-09-14 Petter Holme

Many researchers from a variety of fields including computer science, network science and mathematics have focused on how to contain the outbreaks of Internet misinformation that threaten social systems and undermine societal health. Most…

物理与社会 · 物理学 2019-12-23 Wei Wang , Yuanhui Ma , Tao Wu , Yang Dai , Xingshu Chen , Lidia A. Braunstein

Many progresses in the understanding of epidemic spreading models have been obtained thanks to numerous modeling efforts and analytical and numerical studies, considering host populations with very different structures and properties,…

物理与社会 · 物理学 2018-02-02 Antoine Moinet , Alain Barrat , Romualdo Pastor Satorras

Epidemiological contact network models have emerged as an important tool in understanding and predicting the spread of infectious disease, due to their capacity to engage individual heterogeneity that may underlie essential dynamics of a…

物理与社会 · 物理学 2019-10-28 Jack Leitch , Kathleen A. Alexander , Srijan Sengupta

The spread of a contagious disease clearly depends on when infected individuals come into contact with susceptible ones. Such effects, however, have remained largely unexplored in the study of epidemic outbreaks. In particular, it remains…

Network epidemiology's most important assumption is that the contact structure over which infectious diseases propagate can be represented as a static network. However, contacts are highly dynamic, changing at many time scales. In this…

物理与社会 · 物理学 2015-06-12 Petter Holme

The structure of sexual contacts, its contacts network and its temporal interactions, play an important role in the spread of sexually transmitted infections. Unfortunately, that kind of data is very hard to obtain. One of the few…

物理与社会 · 物理学 2015-06-11 Alexsandro M. Carvalho , Sebastian Goncalves

Much effort has been devoted to understand how temporal network features and the choice of the source node affect the prevalence of a diffusion process. In this work, we addressed the further question: node pairs with what kind of local and…

物理与社会 · 物理学 2018-04-26 Xiu-Xiu Zhan , Alan Hanjalic , Huijuan Wang

Empirical temporal networks display strong heterogeneities in their dynamics, which profoundly affect processes taking place on these networks, such as rumor and epidemic spreading. Despite the recent wealth of data on temporal networks,…

物理与社会 · 物理学 2014-10-13 Christian L. Vestergaard , Mathieu Génois , Alain Barrat

Face-to-face social contacts are potentially important transmission routes for acute respiratory infections, and understanding the contact network can improve our ability to predict, contain, and control epidemics. Although workplaces are…

统计方法学 · 统计学 2015-03-02 Gail E. Potter , Timo Smieszek , Kerstin Sailer

The contact structure between hosts has a critical influence on disease spread. However, most networkbased models used in epidemiology tend to ignore heterogeneity in the weighting of contacts. This assumption is known to be at odds with…

种群与进化 · 定量生物学 2012-09-03 Christel Kamp , Mathieu Moslonka-Lefebvre , Samuel Alizon

We consider the optimization problem of seeding a spreading process on a temporal network so that the expected size of the resulting outbreak is maximized. We frame the problem for a spreading process following the rules of the…

物理与社会 · 物理学 2020-10-20 Sirag Erkol , Dario Mazzilli , Filippo Radicchi

Data of physical contacts and face-to-face communications suggest temporally varying networks as the media on which infections take place among humans and animals. Epidemic processes on temporal networks are complicated by complexity of…

物理与社会 · 物理学 2016-07-12 Leo Speidel , Konstantin Klemm , Víctor M. Eguíluz , Naoki Masuda

Diseases spread over temporal networks of interaction events between individuals. Structures of these temporal networks hold the keys to understanding epidemic propagation. One early concept of the literature to aid in discussing these…

物理与社会 · 物理学 2021-06-07 Naoki Masuda , Joel C. Miller , Petter Holme

We investigate what structural aspects of a collection of twelve empirical temporal networks of human contacts are important to disease spreading. We scan the entire parameter spaces of the two canonical models of infectious disease…

种群与进化 · 定量生物学 2014-05-27 Petter Holme , Fredrik Liljeros

Temporal networks, whose links are activated or deactivated over time, are used to represent complex systems such as social interactions or collaborations occurring at specific times. Such networks facilitate the spread of information and…

社会与信息网络 · 计算机科学 2025-02-27 Tianrui Mao , Shilun Zhang , Alan Hanjalic , Huijuan Wang
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