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Big Data, Digital Transformation and Population Health—in the Era of the Pandemic

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (16 May 2023) | Viewed by 2475

Special Issue Editors


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Guest Editor
Asian International Collaboration, Waitematā District, Te Whatu Ora – Health New Zealand, Private Bag 93-503, Auckland 0740, New Zealand
Interests: surveillance; epidemiologic studies; clinical epidemiology; epidemiological analysis; observational studies; statistical analysis; logistic regression; data analysis; epidemiology; public health; health-service access; health promotion; ethnic health; digital transformation

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Guest Editor
School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland Private Bag 92019, New Zealand
Interests: epidemiology; Asian and ethnic minority health; youth health; observational studies; population health

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Guest Editor
GeoHealth Laboratory, Geospatial Research Institute, University of Canterbury, Christchurch 8041, New Zealand
Interests: big data; public health; health geography/geohealth

Special Issue Information

Dear Colleagues,

This Special Issue of IJERPH calls for papers discussing Big Data, Digital Transformation and Population Health within the context of COVID-19. Since the onset of the pandemic, there has been significant global progress in Big Data, digital transformation and their use in population health and healthcare delivery. Topics related to Big Data and digital transformation during the pandemic include eHealth/telehealth, scanning, testing, COVID tracing, geographical epidemiology, new initiatives and innovations for data sharing, risk communication and management, data privacy, governance and ethics, among others.

Despite the apparent advancements in the use of Big Data and Digital Transformation during the pandemic, given the variation in technological capabilities, and population-level differences in health/internet literacy, there are likely to be disparities in health gains within and across countries.

Our Special Issue seeks to gain a greater understanding of how Big Data and digitally transformed systems within the context of a pandemic could be used more efficiently for greater health gains for our populations. This issue provides an excellent opportunity for those involved in these areas to share their knowledge on the development, the progress, the gains and the lessons learnt.

Dr. Lifeng Zhou
Dr. Roshini Peiris-John
Dr. Matthew Hobbs
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data
  • digital transformation
  • population health/ public health
  • eHealth/telehealth
  • COVID-19
  • health equity
  • vulnerable populations
  • indigenous and ethnic populations
  • data governance and ethics
  • risk management and communication

Published Papers (1 paper)

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Review

24 pages, 13671 KiB  
Review
Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace
by Jun Liu, Shuang Lai, Ayesha Akram Rai, Abual Hassan and Ray Tahir Mushtaq
Int. J. Environ. Res. Public Health 2023, 20(5), 3930; https://doi.org/10.3390/ijerph20053930 - 22 Feb 2023
Cited by 6 | Viewed by 1768
Abstract
In recent years, there has been a growing amount of discussion on the use of big data to prevent and treat pandemics. The current research aimed to use CiteSpace (CS) visual analysis to uncover research and development trends, to help academics decide on [...] Read more.
In recent years, there has been a growing amount of discussion on the use of big data to prevent and treat pandemics. The current research aimed to use CiteSpace (CS) visual analysis to uncover research and development trends, to help academics decide on future research and to create a framework for enterprises and organizations in order to plan for the growth of big data-based epidemic control. First, a total of 202 original papers were retrieved from Web of Science (WOS) using a complete list and analyzed using CS scientometric software. The CS parameters included the date range (from 2011 to 2022, a 1-year slice for co-authorship as well as for the co-accordance assessment), visualization (to show the fully integrated networks), specific selection criteria (the top 20 percent), node form (author, institution, region, reference cited, referred author, journal, and keywords), and pruning (pathfinder, slicing network). Lastly, the correlation of data was explored and the findings of the visualization analysis of big data pandemic control research were presented. According to the findings, “COVID-19 infection” was the hottest cluster with 31 references in 2020, while “Internet of things (IoT) platform and unified health algorithm” was the emerging research topic with 15 citations. “Influenza, internet, China, human mobility, and province” were the emerging keywords in the year 2021–2022 with strength of 1.61 to 1.2. The Chinese Academy of Sciences was the top institution, which collaborated with 15 other organizations. Qadri and Wilson were the top authors in this field. The Lancet journal accepted the most papers in this field, while the United States, China, and Europe accounted for the bulk of articles in this research. The research showed how big data may help us to better understand and control pandemics. Full article
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