Evidence map›Paper›PMID 26528909›Full record

ArticlePloS one2015

The Effects of Media Reports on Disease Spread and Important Public Health Measurements.

Shannon Collinson, Kamran Khan, Jane M Heffernan

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 2 pooled it
3.0field-weighted citation impact, top 9% 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

52 citing papers in PubMed, 2 syntheses or guidelines pooled it, 166 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
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  5. Comparative evaluation of behavioral epidemic models using COVID-19 data.Proceedings of the National Academy of Sciences of the United States of America · 2025
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  13. Research on the Influence of Information Diffusion on the Transmission of the Novel Coronavirus (COVID-19).International journal of environmental research and public health · 2022
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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

3 authors at 2 institutions in 1 country.

Shannon CollinsonModelling Infection and Immunity Lab, Centre for Disease Modelling, York University, Toronto, Canada; Mathematics & Statistics, York University, Toronto, Canada.
Kamran KhanLi Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada; Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada.
Jane M HeffernanModelling Infection and Immunity Lab, Centre for Disease Modelling, York University, Toronto, Canada; Mathematics & Statistics, York University, Toronto, Canada.
York University · CAUniversity of Toronto · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Controlling the spread of influenza to reduce the effects of infection on a population is an important mandate of public health. Mass media reports on an epidemic or pandemic can provide important information to the public, and in turn, can induce positive healthy behaviour practices (i.e., handwashing, social distancing) in the individuals, that will reduce the probability of contracting the disease. Mass media fatigue, however, can dampen these effects. Mathematical models can be used to study the effects of mass media reports on epidemic/pandemic outcomes. In this study we employ a stochastic agent based model to provide a quantification of mass media reports on the variability in important public health measurements. We also include mass media report data compiled by the Global Public Health Intelligence Network, to study the effects of mass media reports in the 2009 H1N1 pandemic. We find that the report rate and the rate at which individuals relax their healthy behaviours (media fatigue) greatly affect the variability in important public health measurements. When the mass media reporting data is included in the model, two peaks of infection result.

Indexed as

Influenza A Virus, H1N1 SubtypeInformation DisseminationModels, TheoreticalPandemicsSocial MediaHumansInfluenza, Human

Identifiers

PMID26528909
PMCPMC4631512
OpenAlexW2106813344

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.