Evidence map›Paper›PMID 25422579›Full record

ArticleIEEE communications letters : a publication of the IEEE Communications Society2014

Optimal Network-based Intervention in the Presence of Undetectable Viruses.

Mina Youssef, Caterina Scoglio

Open access · greenAbstract read
In one paragraph

Article in IEEE communications letters : a publication of the IEEE Communications Society, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.7field-weighted citation impact, top 24% of its field
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

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

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

2 authors at 2 institutions in 1 country.

Mina YoussefNetwork Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24061 USA ( myoussef@vbi.vt.edu ).
Caterina ScoglioThe Institute of Computational Comparative Medicine, College of Veterinary Medicine, and with Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506 USA ( caterina@ksu.edu ).
Kansas State University · USVirginia Tech · US

Funding

Synthetic Information Systems for Better Informing Public Health PolicymakersU01GM070694 · NIGMS · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI EUBANK, STEPHEN G, MARATHE, MADHAV VISHNU · 2004 to 2016
$8.6M
NIGMS NIH HHS U01 GM070694
6 · The paper itself

Abstract

This letter presents an optimal control framework to reduce the spread of viruses in networks. The network is modeled as an undirected graph of nodes and weighted links. We consider the spread of viruses in a network as a system, and the total number of infected nodes as the state of the system, while the control function is the weight reduction leading to slow/reduce spread of viruses. Our epidemic model overcomes three assumptions that were extensively used in the literature and produced inaccurate results. We apply the optimal control formulation to crucial network structures. Numerical results show the dynamical weight reduction and reveal the role of the network structure and the epidemic model in reducing the infection size in the presence of indiscernible infected nodes.

Indexed as

asymptomatic infectionnetworked-based modeloptimal controlSpread of viruses

Identifiers

PMID25422579
PMCPMC4240307
OpenAlexW2061840710

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.