ArticlePloS one2013
An efficient immunization strategy for community networks.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 72 citations in OpenAlex.
- COVID-19 Spread in Saudi Arabia: Modeling, Simulation and Analysis.International journal of environmental research and public health · 2020Article
- Immunization strategies in networks with missing data.PLoS computational biology · 2020Article
- Vaccination strategies on dynamic networks with indirect transmission links and limited contact information.PloS one · 2020Article
- Centrality in Complex Networks with Overlapping Community Structure.Scientific reports · 2019Article
- Article
- Optimization of targeted node set in complex networks under percolation and selection.Physical review. E · 2018Article
- Fragmenting networks by targeting collective influencers at a mesoscopic level.Scientific reports · 2016Article
- A biologically inspired immunization strategy for network epidemiology.Journal of theoretical biology · 2016Article
- Immunization strategy based on the critical node in percolation transition.Physics letters. A · 2015Article
- Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An efficient algorithm that can properly identify the targets to immunize or quarantine for preventing an epidemic in a population without knowing the global structural information is of obvious importance. Typically, a population is characterized by its community structure and the heterogeneity in the weak ties among nodes bridging over communities. We propose and study an effective algorithm that searches for bridge hubs, which are bridge nodes with a larger number of weak ties, as immunizing targets based on the idea of referencing to an expanding friendship circle as a self-avoiding walk proceeds. Applying the algorithm to simulated networks and empirical networks constructed from social network data of five US universities, we show that the algorithm is more effective than other existing local algorithms for a given immunization coverage, with a reduced final epidemic ratio, lower peak prevalence and fewer nodes that need to be visited before identifying the target nodes. The effectiveness stems from the breaking up of community networks by successful searches on target nodes with more weak ties. The effectiveness remains robust even when errors exist in the structure of the networks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.