Epidemic control analysis: designing targeted intervention strategies against epidemics propagated on contact networks.
TLDR
A new approach to design effective targeted intervention strategies to mitigate and control the propagation of infections across heterogeneous contact networks is introduced, using a newly developed individual-level deterministic Susceptible-Infectious-Susceptible (SIS) epidemiological model.About:
This article is published in Journal of Theoretical Biology.The article was published on 2015-01-21 and is currently open access. It has received 24 citations till now. The article focuses on the topics: Population.read more
Citations
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The Web of Human Sexual Contacts
TL;DR: In this article, the authors analyze data on the sexual behavior of a random sample of individuals, and find that the cumulative distributions of the number of sexual partners during the twelve months prior to the survey decays as a power law with similar exponents for females and males.
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A measure of individual role in collective dynamics
TL;DR: In this paper, the authors show that the contribution of a node to the collective behavior is not uniquely determined by the structure of the system but it is a result of the interplay between dynamics and network structure.
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An individual-based modeling framework for infectious disease spreading in clustered complex networks
Qingchu Wu,Tarik Hadzibeganovic +1 more
TL;DR: An individual-based model with dynamical equations for susceptible-infected-susceptible (SIS) epidemics on clustered networks is introduced, finding that clustering can significantly alter the epidemic threshold, depending nontrivially on topological details of the underlying population structure.
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Sparse Resource Allocation for Control of Spreading Processes via Convex Optimization
TL;DR: This letter proposes a method for sparse allocation of resources to control spreading processes – such as epidemics and wildfires – using convex optimization, in particular exponential cone programming.
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An improved acquaintance immunization strategy for complex network.
Li Chen,Dongyi Wang +1 more
TL;DR: This improved acquaintance immunization strategy is based on a new mathematical model Network Structure Index (NSI), which digs deep to measure the connection property and surrounding influence of a node's neighbor nodes to better determine the importance of nodes during immunization.
References
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Collective dynamics of small-world networks
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
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Emergence of Scaling in Random Networks
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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Statistical mechanics of complex networks
TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
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The Structure and Function of Complex Networks
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
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Centrality in social networks conceptual clarification
TL;DR: In this article, three distinct intuitive notions of centrality are uncovered and existing measures are refined to embody these conceptions, and the implications of these measures for the experimental study of small groups are examined.