Continuous-Time Discrete-Distribution Theory for Activity-Driven Networks

Zino, Lorenzo; Rizzo, Alessandro; Porfiri, Maurizio

doi:10.1103/physrevlett.117.228302

Cited by 64 publications

(94 citation statements)

References 35 publications

(32 reference statements)

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“…This can overcome some limitations of the continuous distribution discrete time ADNs and enhance our capabilities. For example, non-exponential inter-event times can be incorporated, and the partition of nodes in several classes based on their activity potentials can help studying the non mean-field dynamics [25].…”

Section: Contact Tracing Performancementioning

confidence: 99%

Quantifying the impact of early-stage contact tracing on controlling Ebola diffusion

Shahtori

Ferdousi²,

Scoglio³

et al. 2018

Mathematical Biosciences &Amp; Engineering

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Recent experience of the Ebola outbreak in 2014 highlighted the importance of immediate response measure to impede transmission in the early stage. To this aim, efficient and effective allocation of limited resources is crucial. Among the standard interventions is the practice of following up with the recent physical contacts of the infected individuals-known as contact tracing. In an effort to understand the effects of contact tracing protocols objectively, we explicitly develop a model of Ebola transmission incorporating contact tracing. Our modeling framework is individual-based, patient-centric, stochastic and parameterizable to suit early-stage Ebola transmission. Notably, we propose an activity driven network approach to contact tracing, and estimate the basic reproductive ratio of the epidemic growth in different scenarios. Exhaustive simulation experiments suggest that early contact tracing paired with rapid hospitalization can effectively impede the epidemic growth. Resource allocation needs to be carefully planned to enable early detection of the contacts and rapid hospitalization of the infected people.

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Section: Contact Tracing Performancementioning

confidence: 99%

Quantifying the impact of early-stage contact tracing on controlling Ebola diffusion

Shahtori

Ferdousi²,

Scoglio³

et al. 2018

Mathematical Biosciences &Amp; Engineering

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“…ones' connections increase with the decrease of their activity rates, the propensity for cooperation can be greatly enhanced.hand, most of the previous researches of evolutionary game were concentrated on non-time-varying networks. Whereas the individuals' interactive networks are not only intertwined, but also evolving with time [34][35][36][37][38][39]. In fact, for the time-varying networks, such as social networks, the structure is usually twofold.…”

mentioning

confidence: 99%

How the weak and strong links affect the evolution of prisoner's dilemma game

Han

2019

New J. Phys.

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The complex interactions between individuals are intertwined with time in multilayer networks. In this paper, we propose a prisoner's dilemma game on a two-layered network, including the weak-link layer and the strong-link layer. Based on the mean-field theory and numerical simulations, we show that if the players update their strategies primarily depending on the information received in the weaklink layer, i.e. the weak relations have the dominate influences on the individuals' strategies choice, the cooperation becomes the most abundant strategy. More comparative analysis of the different structures on the strong-link layer indicates that a higher connectivity heterogeneity of the strong-link layer could be conducive to promote the individuals' cooperation behavior. In the situation while the relation between the players' connections in the strong-link layer and their activity rates in the weaklink layer is negative correlated, i.e. ones' connections increase with the decrease of their activity rates, the propensity for cooperation can be greatly enhanced.hand, most of the previous researches of evolutionary game were concentrated on non-time-varying networks. Whereas the individuals' interactive networks are not only intertwined, but also evolving with time [34][35][36][37][38][39]. In fact, for the time-varying networks, such as social networks, the structure is usually twofold. For one thing, some links are temporally dynamical during network evolution, which can be treated as weak connections between nodes. For another, some links are static and invariant, we treat these as strong connections. For example, we could view the individuals who comment on an event in some online social platform as a link, and when all individuals comment on different news events, different relations are established between the pair of individuals. As the relations may change over time, so we call such varying relations as the weak links. However, an individual's offline relations generally do not change or will not change in a period, such as the kinship or friendship, which we call these constant relations as the strong links [40,41].Weak links and strong links play a very important role in the social structure, and are the effective bridge to transmit information between different social clusters. As a result, the different links of the players would heavily affect the change of their strategies. However, the influence of weak links and strong links on human behavior has remained unclear. In order to uncover how the weak and strong links affect the evolution of players' strategies, we explore the PD game in a two-layered network. We use the activity-driven model to describe the network structure of the weak-link layer [42]. In addition, we analyze and simulate the PD game, mainly focusing on three types of networks of the strong-link layer, which are the lattice network, the ER random network and the BA scare-free network, respectively. If the weak relations have the dominate influences on the individuals' strategy choice, the...

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“…Second, other models that vary the level of concurrency while preserving the mean degree are numerical [10,11,17,18]. In the present study, we use the analytically tractable activity-driven model of temporal networks [19][20][21][22][23] to explicitly modulate the size of the concurrently active network with the structure of the aggregate network fixed. With this machinery, we carefully treat extinction effects, derive an analytically tractable matrix equation using a probability generating function for dynamical networks, and reveal nonmonotonic effects of link concurrency on spreading dynamics.…”

mentioning

confidence: 99%

“…We show that the dynamics of networks can either enhance or suppress infection, depending on the amount of concurrency that individual nodes have. Note that analysis of epidemic processes driven by discrete pairwise contact events, which is a popular approach [1][2][3]9,[23][24][25][26][27], does not address the problem of concurrency because we must be able to control the number of simultaneously active links possessed by a node in order to examine the role of concurrency without confounding with other aspects.…”

mentioning

confidence: 99%

“…Model.-We consider the following continuous-time susceptible-infected-susceptible (SIS) model on a discrete-time variant of activity-driven networks, which is a generative model of temporal networks [19][20][21][22][23]. The number of nodes is denoted by N. Each node i ð1 ≤ i ≤ NÞ is assigned an activity potential a i , drawn from a probability density FðaÞ ð0 < a ≤ 1Þ.…”

mentioning

confidence: 99%

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Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks

2017

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Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations. DOI: 10.1103/PhysRevLett.119.108301 Introduction.-Social contact networks-on which infectious diseases occur in humans and animals or viral information spreads online and offline-are mostly dynamic. Switching of partners and the (usually nonMarkovian) activity of individuals, for example, shape network dynamics on such temporal networks [1][2][3]. A better understanding of epidemic dynamics on temporal networks is needed to help improve predictions of, and interventions in, emergent infectious diseases, to design vaccination strategies, and to identify viral marketing opportunities. This is particularly so because what we know about epidemic processes on static networks [4][5][6][7] is only valid when the time scales of the network dynamics and of the infectious processes are well separated. In fact, temporal properties of networks, such as long-tailed distributions of intercontact times, temporal and cross-edge correlation in intercontact times, and entries and exits of nodes, considerably alter how infections spread in a network [1][2][3]8,9].In the present study, we focus on a relatively neglected component of temporal networks, i.e., the number of concurrent contacts that a node has. Even if two temporal networks are the same when aggregated over a time horizon, they may be different as temporal networks due to different levels of concurrency. Concurrency is a longstanding concept in epidemiology, in particular in the context of monogamy or polygamy affecting sexually transmitted infections [10][11][12]. Modeling studies to date largely agree that a level of high concurrency (e.g., polygamy as opposed to monogamy) enhances epidemic spreading in a population. However, this finding, while intuitive, lacks theoretical underpinning. First, some models assume that the mean degree, or equivalently the average contact rate, of nodes increases as the concurrency increases [13][14][15][16]. In these cases, the observed enhancement in epidemic spreading is an obvious outcome of a higher density of edges rather than a high concurrency. Second, other models that vary the level

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Continuous-Time Discrete-Distribution Theory for Activity-Driven Networks

Cited by 64 publications

References 35 publications

Quantifying the impact of early-stage contact tracing on controlling Ebola diffusion

Quantifying the impact of early-stage contact tracing on controlling Ebola diffusion

How the weak and strong links affect the evolution of prisoner's dilemma game

Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks

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