Suppressing Epidemic Spreading via Contact Blocking in Temporal Networks

2020 ◽  
pp. 444-454
Author(s):  
Xunyi Zhao ◽  
Huijuan Wang
Keyword(s):  
Temporal Networks ◽  
Epidemic Spreading

Epidemic Spreading

2018 ◽  
Author(s):  
Ginestra Bianconi
Keyword(s):  
Social Media ◽  
Infectious Diseases ◽  
Sir Model ◽  
Temporal Networks ◽  
Epidemic Spreading ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Sis Model ◽  
Online Social Media ◽  
Different Types

Epidemic processes are relevant to studying the propagation of infectious diseases, but their current use extends also to the study of propagation of ideas in the society or memes and news in online social media. In most of the relevant applications epidemic spreading does not actually take place on a single network but propagates in a multilayer network where different types of interaction play different roles. This chapter provides a comprehensive view of the effect that multilayer network structures have on epidemic processes. The Susceptible–Infected–Susceptible (SIS) Model and the Susceptible–Infected–Removed (SIR) Model are characterized on multilayer networks. Additionally, it is shown that the multilayer networks framework can also allow us to study interacting Awareness and epidemic spreading, competing networks and epidemics in temporal networks.

scholarly journals Effect of risk perception on epidemic spreading in temporal networks

2018 ◽  
Vol 97 (1) ◽  
Author(s):  
Antoine Moinet ◽  
Romualdo Pastor-Satorras ◽  
Alain Barrat
Keyword(s):  
Risk Perception ◽  
Temporal Networks ◽  
Epidemic Spreading

scholarly journals Dynamics of cascades on burstiness-controlled temporal networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Samuel Unicomb ◽  
Gerardo Iñiguez ◽  
James P. Gleeson ◽  
Márton Karsai
Keyword(s):  
Information Diffusion ◽  
Broad Class ◽  
Social Systems ◽  
Analytical Framework ◽  
Universal Curve ◽  
Temporal Networks ◽  
Epidemic Spreading ◽  
Interevent Time ◽  
Generic Dynamics ◽  
Temporal Interactions

AbstractBurstiness, the tendency of interaction events to be heterogeneously distributed in time, is critical to information diffusion in physical and social systems. However, an analytical framework capturing the effect of burstiness on generic dynamics is lacking. Here we develop a master equation formalism to study cascades on temporal networks with burstiness modelled by renewal processes. Supported by numerical and data-driven simulations, we describe the interplay between heterogeneous temporal interactions and models of threshold-driven and epidemic spreading. We find that increasing interevent time variance can both accelerate and decelerate spreading for threshold models, but can only decelerate epidemic spreading. When accounting for the skewness of different interevent time distributions, spreading times collapse onto a universal curve. Our framework uncovers a deep yet subtle connection between generic diffusion mechanisms and underlying temporal network structures that impacts a broad class of networked phenomena, from spin interactions to epidemic contagion and language dynamics.

scholarly journals Epidemic spreading and aging in temporal networks with memory

2018 ◽  
Vol 98 (6) ◽  
Author(s):  
Michele Tizzani ◽  
Simone Lenti ◽  
Enrico Ubaldi ◽  
Alessandro Vezzani ◽  
Claudio Castellano ◽  
...  
Keyword(s):  
Temporal Networks ◽  
Epidemic Spreading ◽  
With Memory

scholarly journals Data Summaries and Representations: Definitions and Practical Use

2018 ◽  
pp. 101-115
Author(s):  
Alain Barrat ◽  
Ciro Cattuto
Keyword(s):  
Social Networks ◽  
Data Structures ◽  
Temporal Evolution ◽  
Use Cases ◽  
Data Driven ◽  
Temporal Networks ◽  
Epidemic Spreading ◽  
Concrete Case ◽  
Data Representations ◽  
Data Summaries

The chapter “Data Summaries and Representations: Definitions and Practical Use” examines data structures used to deal with complex networked data, using temporal networks as a concrete case. Complex networked data has become available in a variety of contexts, describing a variety of systems with growing abundance of details, such as, for instance, links between individuals in social networks, or the temporal evolution of these links. However, data needs to be summarized and represented in simple forms. This chapter describes several commonly used data summaries and levels of representation of temporal networks, as well as novel data representations that have been developed through the MULTIPLEX project. It focuses in particular on the case of temporal networks of contacts between individuals and shows in a series of concrete use cases how different representations can be used to characterize and compare data, or feed data-driven models of epidemic spreading processes.

scholarly journals Change points, memory and epidemic spreading in temporal networks

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Tiago P. Peixoto ◽  
Laetitia Gauvin
Keyword(s):  
Change Points ◽  
Temporal Networks ◽  
Epidemic Spreading

scholarly journals Contact-Based Model for Epidemic Spreading on Temporal Networks

2019 ◽  
Vol 9 (3) ◽  
Author(s):  
Andreas Koher ◽  
Hartmut H. K. Lentz ◽  
James P. Gleeson ◽  
Philipp Hövel
Keyword(s):  
Temporal Networks ◽  
Epidemic Spreading

scholarly journals Mitigate SIR epidemic spreading via contact blocking in temporal networks

2022 ◽  
Vol 7 (1) ◽  
Author(s):  
Shilun Zhang ◽  
Xunyi Zhao ◽  
Huijuan Wang
Keyword(s):  
Contact Network ◽  
Temporal Networks ◽  
Epidemic Spreading ◽  
Node Pair ◽  
Component Size ◽  
Human Contact ◽  
Sir Epidemic ◽  
Centrality Metrics ◽  
Random Removal ◽  
Made In

AbstractProgress has been made in how to suppress epidemic spreading on temporal networks via blocking all contacts of targeted nodes or node pairs. In this work, we develop contact blocking strategies that remove a fraction of contacts from a temporal (time evolving) human contact network to mitigate the spread of a Susceptible-Infected-Recovered epidemic. We define the probability that a contact c(i, j, t) is removed as a function of a given centrality metric of the corresponding link l(i, j) in the aggregated network and the time t of the contact. The aggregated network captures the number of contacts between each node pair. A set of 12 link centrality metrics have been proposed and each centrality metric leads to a unique contact removal strategy. These strategies together with a baseline strategy (random removal) are evaluated in empirical contact networks via the average prevalence, the peak prevalence and the time to reach the peak prevalence. We find that the epidemic spreading can be mitigated the best when contacts between node pairs that have fewer contacts and early contacts are more likely to be removed. A strategy tends to perform better when the average number contacts removed from each node pair varies less. The aggregated pruned network resulted from the best contact removal strategy tends to have a large largest eigenvalue, a large modularity and probably a small largest connected component size.

Probabilistic Temporal Networks.

1996 ◽  
Author(s):  
Eugene Santos ◽  
Young Jr. ◽  
Joel D.
Keyword(s):  
Temporal Networks
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