Predicting and controlling infectious disease epidemics using temporal networks

Masuda, Naoki; Holme, Petter

doi:10.12703/p5-6

Cited by 185 publications

(189 citation statements)

References 106 publications

(110 reference statements)

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“…In other words, the mechanisms driving the formation of contacts at the time-scale of minutes are different than those at the time scale of months [675]. This is of particular importance when studying the spreading of infectious diseases [230,593,596,684,[686][687][688][689][690][691][692][693][694][695][696][697][698][699][700][701][702][703][704]. Indeed, contacts dynamics that affect the spreading of transmissible illnesses are those unfolding at comparable time-scale respect to the disease [219,652,676,686].…”

Section: Measuring and Understanding Close Proximity Interactionsmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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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.

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Section: Measuring and Understanding Close Proximity Interactionsmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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“…Relevant examples are epidemic spreading, human communication or transportation networks, i.e. systems where the strong time variability plays a crucial role in determining connections and interactions [4][5][6]. Also, approaching continuous dynamical systems from a network perspective [7,8] requires a spatio-temporal discretization that, in the non-autonomous case, often determines a marked time-dependence of the resulting networks.…”

Section: Introductionmentioning

confidence: 99%

Most probable paths in temporal weighted networks: An application to ocean transport

et al. 2015

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We consider paths in weighted and directed temporal networks, introducing tools to compute sets of paths of high probability. We quantify the relative importance of the most probable path between two nodes with respect to the whole set of paths, and to a subset of highly probable paths which incorporate most of the connection probability. These concepts are used to provide alternative definitions of betweenness centrality. We apply our formalism to a transport network describing surface flow in the Mediterranean sea. Despite the full transport dynamics is described by a very large number of paths we find that, for realistic time scales, only a very small subset of high probability paths (or even a single most probable one) is enough to characterize global connectivity properties of the network.

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“…In time-varying networks the analytical study of contagion processes is hindered by the difficulties in dealing with the concurrent time scales of the contagion and network evolution processes. [34][35][36][37][38][39]. In the case of activity driven networks however it is possible to derive the mean-field level dynamical equations describing the contagion process by defining the activity block variable I t a and S t a that represent the number of infected and susceptible individuals, respectively, in the class of activity a at time t. From those quantity it is possible to derive the mean-field evolution of the number of infected individuals of class a at time t + 1 as…”

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confidence: 99%

Controlling Contagion Processes in Activity Driven Networks

et al. 2014

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The vast majority of strategies aimed at controlling contagion processes on networks considers the connectivity pattern of the system as either quenched or annealed. However, in the real world many networks are highly dynamical and evolve in time concurrently to the contagion process. Here, we derive an analytical framework for the study of control strategies specifically devised for time-varying networks. We consider the removal/immunization of individual nodes according the their activity in the network and develop a block variable mean-field approach that allows the derivation of the equations describing the evolution of the contagion process concurrently to the network dynamic. We derive the critical immunization threshold and assess the effectiveness of the control strategies. Finally, we validate the theoretical picture by simulating numerically the information spreading process and control strategies in both synthetic networks and a large-scale, real-world mobile telephone call dataset.The spreading of infectious diseases, malwares, scientific ideas, memes are just few example of real world phenomena that can be modeled as contagion processes on networks [1][2][3][4]. It has long been acknowledged that network structures and connectivity patterns are a relevant factor in determining the properties of spreading processes. A number of strategies aimed at controlling them have been proposed with the aim of improving on the random removal strategies that originally defined the concept of herd immunity. Those strategies target nodes according the number of connections (degree), the k-core or the betweenness of nodes, just to mention a few examples [5,6]. The efficiency of each strategy is then measured by its effect on the contagion process when a fraction p of nodes is removed from the system. More precisely, the smaller is the fraction p of nodes removed in order to halt the contagion process and the more effective is the strategy. Although most real world networks show a high level of dynamic activity, the large majority of theoretical results concerning the control of contagion processes have been obtained by using a complete timescale separation between the contagion process, τ P , and the change in network's structure, τ G . In these approaches the dynamical process takes place in either static (τ P τ G ) or annealed (τ P τ G ) networks. However, when the two time scales are comparable these convenient approximations might introduce uncontrolled biases in the correct characterization of the dynamical properties of the contagion process . Here we investigate the effect of time-varying connectivity pattern of networks on contagion control strategies by considering the specific class of activity driven network models [23]. In particular, we consider the susceptibleinfected-susceptible (SIS) contagion model [30] and derive analytically its critical immunization threshold in three different control strategies. We also validate the findings obtained in synthetic networks by studying the effect of each strategy...

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Predicting and controlling infectious disease epidemics using temporal networks

Cited by 185 publications

References 106 publications

Statistical physics of vaccination

Statistical physics of vaccination

Most probable paths in temporal weighted networks: An application to ocean transport

Controlling Contagion Processes in Activity Driven Networks

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