Evidence map›Paper›PMID 28572623›Full record

ArticleScientific reports2017

Voluntary Vaccination through Self-organizing Behaviors on Locally-mixed Social Networks.

Benyun Shi, Hongjun Qiu, Wenfang Niu, Yizhi Ren, Hong Ding, Dan Chen

Abstract read
In one paragraph

Article in Scientific reports, 2017. 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
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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

6 authors.

Benyun ShiSchool of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China. benyunshi@outlook.com.
Hongjun QiuSchool of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China.
Wenfang NiuSchool of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China.
Yizhi RenSchool of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China. renyz@hdu.edu.cn.
Hong DingSchool of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China.
Dan ChenSchool of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Voluntary vaccination reflects how individuals weigh the risk of infection and the cost of vaccination against the spread of vaccine-preventable diseases, such as smallpox and measles. In a homogeneously mixing population, the infection risk of an individual depends largely on the proportion of vaccinated individuals due to the effects of herd immunity. While in a structured population, the infection risk can also be affected by the structure of individuals' social network. In this paper, we focus on studying individuals' self-organizing behaviors under the circumstance of voluntary vaccination in different types of social networks. Specifically, we assume that each individual together with his/her neighbors forms a local well-mixed environment, where individuals meet equally often as long as they have a common neighbor. We carry out simulations on four types of locally-mixed social networks to investigate the network effects on voluntary vaccination. Furthermore, we also evaluate individuals' vaccinating decisions through interacting with their "neighbors of neighbors". The results and findings of this paper provide a new perspective for vaccination policy-making by taking into consideration human responses in complex social networks.

Indexed as

Social BehaviorSocial EnvironmentSocial NetworkingVaccinationComputer SimulationDisease Transmission, InfectiousHumansImmunity, HerdModels, Biological

Identifiers

PMID28572623
PMCPMC5453996

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.