Evidence map›Paper›PMID 28507229›Full record

ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2017

Spread of competing viruses on heterogeneous networks.

Shanshan Chen, Kaihua Wang, Mengfeng Sun, Xinchu Fu

Abstract read
In one paragraph

Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Mathematical methods in medicine: neuroscience, cardiology and pathology.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2017
    Review
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

4 authors.

Shanshan ChenDepartment of Mathematics, Shanghai University, Shanghai 200444, People's Republic of China.
Kaihua WangCollege of Mathematics and Statistics, Hainan Normal University, Haikou 571158, People's Republic of China.
Mengfeng SunDepartment of Mathematics, Shanghai University, Shanghai 200444, People's Republic of China.
Xinchu FuDepartment of Mathematics, Shanghai University, Shanghai 200444, People's Republic of China xcfu@shu.edu.cn.ORCID http://orcid.org/0000-0003-4936-0384

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we propose a model where two strains compete with each other at the expense of common susceptible individuals on heterogeneous networks by using pair-wise approximation closed by the probability-generating function (PGF). All of the strains obey the susceptible-infected-recovered (SIR) mechanism. From a special perspective, we first study the dynamical behaviour of an SIR model closed by the PGF, and obtain the basic reproduction number via two methods. Then we build a model to study the spreading dynamics of competing viruses and discuss the conditions for the local stability of equilibria, which is different from the condition obtained by using the heterogeneous mean-field approach. Finally, we perform numerical simulations on Barabási-Albert networks to complement our theoretical research, and show some dynamical properties of the model with competing viruses.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'.

Indexed as

Models, StatisticalComputer SimulationContact TracingDisease OutbreaksDisease SusceptibilityHumansPrevalenceRisk AssessmentVirus Diseasescompeting virusesepidemic thresholdpair-wisethe basic reproduction number

Identifiers

PMID28507229
PMCPMC5434075

What OpenQuestion holds

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LicenceCC BY
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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.