Control Strategies of Contagion Processes in Time-Varying Networks

Karsai, Márton; Perra, Nicola

doi:10.1007/978-981-10-5287-3_8

Cited by 3 publications

(1 citation statement)

References 94 publications

(107 reference statements)

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“…Different global (eigenvector centrality, degree, coreness, betweenness) and local (random acquaintance [11]) network measures have been used to propose surveillance and vaccination strategies in static [8,12,13] and temporally varying [9,14] networks. Thus, a priori network characterization can be used to identify candidate sites for detecting outbreaks, either in static [1,2,5,[15][16][17], temporal [18,19], dynamic [20] or adaptive networks [21]. Moreover, these measures and their correlations are predictive of epidemic spread and can facilitate rapid targeting of interventions once an outbreak starts [22,23].…”

Section: Introductionmentioning

confidence: 99%

Cattle transport network predicts endemic and epidemic foot-and-mouth disease risk on farms in Turkey

et al. 2022

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The structure of contact networks affects the likelihood of disease spread at the population scale and the risk of infection at any given node. Though this has been well characterized for both theoretical and empirical networks for the spread of epidemics on completely susceptible networks, the long-term impact of network structure on risk of infection with an endemic pathogen, where nodes can be infected more than once, has been less well characterized. Here, we analyze detailed records of the transportation of cattle among farms in Turkey to characterize the global and local attributes of the directed—weighted shipments network between 2007-2012. We then study the correlations between network properties and the likelihood of infection with, or exposure to, foot-and-mouth disease (FMD) over the same time period using recorded outbreaks. The shipments network shows a complex combination of features (local and global) that have not been previously reported in other networks of shipments; i.e. small-worldness, scale-freeness, modular structure, among others. We find that nodes that were either infected or at high risk of infection with FMD (within one link from an infected farm) had disproportionately higher degree, were more central (eigenvector centrality and coreness), and were more likely to be net recipients of shipments compared to those that were always more than 2 links away from an infected farm. High in-degree (i.e. many shipments received) was the best univariate predictor of infection. Low in-coreness (i.e. peripheral nodes) was the best univariate predictor of nodes always more than 2 links away from an infected farm. These results are robust across the three different serotypes of FMD observed in Turkey and during periods of low-endemic prevalence and high-prevalence outbreaks.

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Section: Introductionmentioning

confidence: 99%

Cattle transport network predicts endemic and epidemic foot-and-mouth disease risk on farms in Turkey

et al. 2022

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Dynamical immunization based on random-walk in time-varying networks

Wang

Zeng

Han

2022

Chaos, Solitons & Fractals

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Temporal Cascade Model for Analyzing Spread in Evolving Networks

Haldar

Wang

Demirci

et al. 2023

ACM Trans. Spatial Algorithms Syst.

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Current approaches for modeling propagation in networks (e.g., of diseases, computer viruses, rumors) cannot adequately capture temporal properties such as order/duration of evolving connections or dynamic likelihoods of propagation along connections. Temporal models on evolving networks are crucial in applications that need to analyze dynamic spread. For example, a disease spreading virus has varying transmissibility based on interactions between individuals occurring with different frequency, proximity, and venue population density. Similarly, propagation of information having a limited active period, such as rumors, depends on the temporal dynamics of social interactions. To capture such behaviors, we first develop the Temporal Independent Cascade (T-IC) model with a spread function that efficiently utilizes a hypergraph-based sampling strategy and dynamic propagation probabilities. We prove this function to be submodular, with guarantees of approximation quality. This enables scalable analysis on highly granular temporal networks where other models struggle, such as when the spread across connections exhibits arbitrary temporally evolving patterns. We then introduce the notion of ‘reverse spread’ using the proposed T-IC processes, and develop novel solutions to identify both sentinel/detector nodes and highly susceptible nodes. Extensive analysis on real-world datasets shows that the proposed approach significantly outperforms the alternatives in modeling both if and how spread occurs, by considering evolving network topology alongside granular contact/interaction information. Our approach has numerous applications, such as virus/rumor/influence tracking. Utilizing T-IC, we explore vital challenges of monitoring the impact of various intervention strategies over real spatio-temporal contact networks where we show our approach to be highly effective.

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Control Strategies of Contagion Processes in Time-Varying Networks

Cited by 3 publications

References 94 publications

Cattle transport network predicts endemic and epidemic foot-and-mouth disease risk on farms in Turkey

Cattle transport network predicts endemic and epidemic foot-and-mouth disease risk on farms in Turkey

Dynamical immunization based on random-walk in time-varying networks

Temporal Cascade Model for Analyzing Spread in Evolving Networks

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