Evidence map›Paper›PMID 36711394›Full record

ArticleFrontiers in public health2022

Research on emergency management of global public health emergencies driven by digital technology: A bibliometric analysis.

Chao Wen, Wei Liu, Zhihao He, Chunyan Liu

Abstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

4 authors.

Chao WenSchool of Emergency Management, Xihua University, Chengdu, China.
Wei LiuCollege of Management Science, Chengdu University of Technology, Chengdu, China.
Zhihao HeSchool of Emergency Management, Xihua University, Chengdu, China.
Chunyan LiuSchool of Automation and Electrical Engineering, Chengdu Institute of Technology, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The frequent occurrence of major public health emergencies globally poses a threat to people's life, health, and safety, and the convergence development of digital technology is very effective and necessary to cope with the outbreak and transmission control of public epidemics such as COVID-19, which is essential to improve the emergency management capability of global public health emergencies. Methods: The published literatures in the Web of Science Core Collection database from 2003 to 2022 were utilized to analyze the contribution and collaboration of the authors, institutions, and countries, keyword co-occurrence analysis, and research frontier identification using the CiteSpace, VOSviewer, and COOC software. Results: The results are shown as follows: (1) Relevant research can be divided into growth and development period and rapid development period, and the total publications show exponential growth, among which the USA, China, and the United Kingdom are the most occupied countries, but the global authorship cooperation is not close; (2) clustering analysis of high-frequency keyword, all kinds of digital technologies are utilized, ranging from artificial intelligence (AI)-driven machine learning (ML) or deep learning (DL), and focused application big data analytics and blockchain technology enabled the internet of things (IoT) to identify, and diagnose major unexpected public diseases are hot spots for future research; (3) Research frontier identification indicates that data analysis in social media is a frontier issue that must continue to be focused on to advance digital and smart governance of public health events. Conclusion: This bibliometric study provides unique insights into the role of digital technologies in the emergency management of public health. It provides research guidance for smart emergency management of global public health emergencies.

Indexed as

COVID-19Public HealthArtificial IntelligenceBibliometricsEmergenciesHumansbibliometricsbig datadigital technologyemergency managementmachine learningpublic health

Identifiers

PMID36711394
PMCPMC9875008

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.