Evidence map›Paper›PMID 25419231›Full record

ArticleJournal of physics. A, Mathematical and theoretical2014

Epidemics with temporary link deactivation in scale-free networks.

Maxim S Shkarayev, Ilker Tunc, Leah B Shaw

Abstract read
In one paragraph

Article in Journal of physics. A, Mathematical and theoretical, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Maxim S ShkarayevDepartment of Physics & Astronomy, Iowa State University, Ames IA, 50011.
Ilker TuncJohn P. Hussman Institute for Human Genomics, University of Miami, Miami, FL 33156.
Leah B ShawDepartment of Applied Science, College of William and Mary, Williamsburg, VA 23187.

Funding

Multi-scale modeling of infectious diseases in fluctuating environmentsR01GM090204 · NIGMS · MONTCLAIR STATE UNIVERSITY · PI BILLINGS, LORA · 2009 to 2011
$791k
NIGMS NIH HHS R01 GM090204
6 · The paper itself

Abstract

During an epidemic, people may adapt or alter their social contacts to avoid infection. Various adaptation mechanisms have been studied previously. Recently, a new adaptation mechanism was presented in [1], where susceptible nodes temporarily deactivate their links to infected neighbors and reactivate when their neighbors recover. Considering the same adaptation mechanism on a scale-free network, we find that the topology of the subnetwork consisting of active links is fundamentally different from the original network topology. We predict the scaling exponent of the active degree distribution and derive mean-field equations by using improved moment closure approximations based on the conditional distribution of active degree given the total degree. These mean field equations show better agreement with numerical simulation results than the standard mean field equations based on a homogeneity assumption.

Indexed as

adaptive networksepidemics modelmoment-closure approximation

Identifiers

PMID25419231
PMCPMC4238289

What OpenQuestion holds

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Registered trials

None linked

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.