Evidence map›Paper›PMID 28934352›Full record

ArticlePloS one2017

Global reaction to the recent outbreaks of Zika virus: Insights from a Big Data analysis.

Nicola Luigi Bragazzi, Cristiano Alicino, Cecilia Trucchi, Chiara Paganino, Ilaria Barberis, Mariano Martini, Laura Sticchi, Eugen Trinka, Francesco Brigo, Filippo Ansaldi and 2 more

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
–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

31 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. How COVID-19 Pandemic Has Influenced Public Interest in Foods: A Google Trends Analysis of Italian Data.International journal of environmental research and public health · 2023
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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

12 authors.

Nicola Luigi BragazziDepartment of Health Sciences, University of Genoa, Genoa, Italy.
Cristiano AlicinoDepartment of Health Sciences, University of Genoa, Genoa, Italy.ORCID http://orcid.org/0000-0002-6473-9870
Cecilia TrucchiDepartment of Health Sciences, University of Genoa, Genoa, Italy.
Chiara PaganinoDepartment of Health Sciences, University of Genoa, Genoa, Italy.
Ilaria BarberisDepartment of Health Sciences, University of Genoa, Genoa, Italy.
Mariano MartiniDepartment of Health Sciences, University of Genoa, Genoa, Italy.
Laura SticchiDepartment of Health Sciences, University of Genoa, Genoa, Italy.
Eugen TrinkaDepartment of Neurology, Christian Doppler Klinik, Paracelsus Medical University, Center for Cognitive Neuroscience, Salzburg, Austria.
Francesco BrigoDepartment of Neurology, Franz Tappeiner Hospital, Merano, Italy.
Filippo AnsaldiDepartment of Health Sciences, University of Genoa, Genoa, Italy.
Giancarlo IcardiDepartment of Health Sciences, University of Genoa, Genoa, Italy.
Andrea OrsiDepartment of Health Sciences, University of Genoa, Genoa, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe recent spreading of Zika virus represents an emerging global health threat. As such, it is attracting public interest worldwide, generating a great amount of related Internet searches and social media interactions. The aim of this research was to understand Zika-related digital behavior throughout the epidemic spreading and to assess its consistence with real-world epidemiological data, using a behavioral informatics and analytics approach.

methodsIn this study, the global web-interest and reaction to the recently occurred outbreaks of the Zika Virus were analyzed in terms of tweets and Google Trends (GT), Google News, YouTube, and Wikipedia search queries. These data streams were mined from 1st January 2004 to 31st October 2016, with a focus on the period November 2015-October 2016. This analysis was complemented with the use of epidemiological data. Spearman's correlation was performed to correlate all Zika-related data. Moreover, a multivariate regression was performed using Zika-related search queries as a dependent variable, and epidemiological data, number of inhabitants in 2015 and Human Development Index as predictor variables.

resultsOverall 3,864,395 tweets, 284,903 accesses to Wikipedia pages dedicated to the Zika virus were analyzed during the study period. All web-data sources showed that the main spike of researches and interactions occurred in February 2016 with a second peak in August 2016. All novel data streams-related activities increased markedly during the epidemic period with respect to pre-epidemic period when no web activity was detected. Correlations between data from all these web platforms resulted very high and statistically significant. The countries in which web searches were particularly concentrated are mainly from Central and South Americas. The majority of queries concerned the symptoms of the Zika virus, its vector of transmission, and its possible effect to babies, including microcephaly. No statistically significant correlation was found between novel data streams and global real-world epidemiological data. At country level, a correlation between the digital interest towards the Zika virus and Zika incidence rate or microcephaly cases has been detected.

conclusionsAn increasing public interest and reaction to the current Zika virus outbreak was documented by all web-data sources and a similar pattern of web reactions has been detected. The public opinion seems to be particularly worried by the alert of teratogenicity of the Zika virus. Stakeholders and health authorities could usefully exploited these internet tools for collecting the concerns of public opinion and reply to them, disseminating key information.

Indexed as

Disease OutbreaksInformaticsInternationalityHumansInternetSocial MediaZika VirusZika Virus Infection

Identifiers

PMID28934352
PMCPMC5608413

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.