Evidence map›Paper›PMID 32511260›Full record

ArticlePloS one2020

Quantify the role of superspreaders -opinion leaders- on COVID-19 information propagation in the Chinese Sina-microblog.

Fulian Yin, Xinyu Xia, Nan Song, Lingyao Zhu, Jianhong Wu

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Observational
  6. Article
  7. Article
  8. Article
  9. Article
  10. Fast Response to Superspreading: Uncertainty and Complexity in the Context of COVID-19.International journal of environmental research and public health · 2020
    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

5 authors.

Fulian YinCollege of Information and Communication Engineering, Communication University of China, Beijing, PR China.
Xinyu XiaCollege of Information and Communication Engineering, Communication University of China, Beijing, PR China.
Nan SongCollege of Information and Communication Engineering, Communication University of China, Beijing, PR China.
Lingyao ZhuCollege of Information and Communication Engineering, Communication University of China, Beijing, PR China.
Jianhong WuFields-CQAM Laboratory of Mathematics for Public Health, Laboratory for Industrial and Applied Mathematics, York University, Toronto, Canada.ORCID 0000-0002-3037-7154

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroudEffective communication of accurate information through social media constitutes an important component of public health interventions in modern time, when traditional public health approaches such as contact tracing, quarantine and isolation are among the few options for the containing the disease spread in the population. The success of control of COVID-19 outbreak started from Wuhan, the capital city of Hubei Province of China relies heavily on the resilience of residents to follow public health interventions which induce substantial interruption of social-economic activities, and evidence shows that opinion leaders have been playing significant roles in the propagation of epidemic information and public health policy and implementations.

methodsWe design a mathematical model to quantify the roles of information superspreaders in single specific information which outbreaks rapidly and usually has a short duration period, and to examine the information propagation dynamics in the Chinese Sina-microblog. Our opinion-leader susceptible-forwarding-immune (OL-SFI) model is formulated to track the temporal evolution of forwarding quantities generated by opinion leaders and normal users.

resultsData fitting from the real data of COVID-19 obtained from Chinese Sina-microblog can identify the different contact rates and forwarding probabilities (and hence calculate the basic information forwarding reproduction number of superspreaders), and can be used to evaluate the roles of opinion leaders in different stages of the information propagation and the outbreak unfolding.

conclusionsThe parameterized model can be used to nearcast the information propagation trend, and the model-based sensitivity analysis can help to explore important factors for the roles of opinion leaders.

Indexed as

BloggingHealth CommunicationModels, TheoreticalPublic HealthSocial MediaBetacoronavirusChinaCoronavirus InfectionsCOVID-19Disease OutbreaksHumansPandemicsPneumonia, ViralSARS-CoV-2

Identifiers

PMID32511260
PMCPMC7279582

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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.