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Bibliometric Analysis on Utilization of New Information Technology in the Prevention and Control of COVID-19 — China, 2020

INTRODUCTION: New information technology (IT) has been applied to prevent and control coronavirus disease 2019 (COVID-19) in many regions of China since the outbreak of COVID-19. This study aims to illustrate the current status and key areas of the application of new IT in the prevention and control...

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Detalles Bibliográficos
Autores principales: Li, Dan, Wang, Songwang, Su, Xuemei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8392999/
https://www.ncbi.nlm.nih.gov/pubmed/34595035
http://dx.doi.org/10.46234/ccdcw2021.047
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author Li, Dan
Wang, Songwang
Su, Xuemei
author_facet Li, Dan
Wang, Songwang
Su, Xuemei
author_sort Li, Dan
collection PubMed
description INTRODUCTION: New information technology (IT) has been applied to prevent and control coronavirus disease 2019 (COVID-19) in many regions of China since the outbreak of COVID-19. This study aims to illustrate the current status and key areas of the application of new IT in the prevention and control of COVID-19. METHODS: Literature related to the prevention and control of COVID-19 with new IT since 2020 was retrieved from China National Knowledge Internet and Wanfang Literature databases, the two most authoritative databases in China. CiteSpace 5.7.R2 was used to analyze the institutions, authors, and keywords of the articles. The application of new IT is determined by keywords and highly cited documents. RESULTS: A total of 1,711 articles were included, as the number of publications has been continually increasing over the investigation period. The six hot topics of new IT applied in COVID-19 were big data, artificial intelligence, Internet+, blockchain, Internet of Things, and 5G. The most productive institution is University of Chinese Academy of Sciences, and the most productive author in this field is Tao Pei, whose article, “Multi-level Spatial Distribution Estimation Model of the Inter-Regional Migrant Population Using Multi-Source Spatio-Temporal Big Data: A Case Study of Migrants from Wuhan During the Spread of COVID-19,” was highly cited. DISCUSSION: This study could help medical professionals understand the application status and research trends of new IT in the prevention and control of COVID-19. This paper also helps researchers find potential co-operative institutions and partners.
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spelling pubmed-83929992021-09-29 Bibliometric Analysis on Utilization of New Information Technology in the Prevention and Control of COVID-19 — China, 2020 Li, Dan Wang, Songwang Su, Xuemei China CDC Wkly Methods and Applications INTRODUCTION: New information technology (IT) has been applied to prevent and control coronavirus disease 2019 (COVID-19) in many regions of China since the outbreak of COVID-19. This study aims to illustrate the current status and key areas of the application of new IT in the prevention and control of COVID-19. METHODS: Literature related to the prevention and control of COVID-19 with new IT since 2020 was retrieved from China National Knowledge Internet and Wanfang Literature databases, the two most authoritative databases in China. CiteSpace 5.7.R2 was used to analyze the institutions, authors, and keywords of the articles. The application of new IT is determined by keywords and highly cited documents. RESULTS: A total of 1,711 articles were included, as the number of publications has been continually increasing over the investigation period. The six hot topics of new IT applied in COVID-19 were big data, artificial intelligence, Internet+, blockchain, Internet of Things, and 5G. The most productive institution is University of Chinese Academy of Sciences, and the most productive author in this field is Tao Pei, whose article, “Multi-level Spatial Distribution Estimation Model of the Inter-Regional Migrant Population Using Multi-Source Spatio-Temporal Big Data: A Case Study of Migrants from Wuhan During the Spread of COVID-19,” was highly cited. DISCUSSION: This study could help medical professionals understand the application status and research trends of new IT in the prevention and control of COVID-19. This paper also helps researchers find potential co-operative institutions and partners. Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2021-02-19 /pmc/articles/PMC8392999/ /pubmed/34595035 http://dx.doi.org/10.46234/ccdcw2021.047 Text en Copyright and License information: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2021 https://creativecommons.org/licenses/by-nc-sa/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ (https://creativecommons.org/licenses/by-nc-sa/4.0/)
spellingShingle Methods and Applications
Li, Dan
Wang, Songwang
Su, Xuemei
Bibliometric Analysis on Utilization of New Information Technology in the Prevention and Control of COVID-19 — China, 2020
title Bibliometric Analysis on Utilization of New Information Technology in the Prevention and Control of COVID-19 — China, 2020
title_full Bibliometric Analysis on Utilization of New Information Technology in the Prevention and Control of COVID-19 — China, 2020
title_fullStr Bibliometric Analysis on Utilization of New Information Technology in the Prevention and Control of COVID-19 — China, 2020
title_full_unstemmed Bibliometric Analysis on Utilization of New Information Technology in the Prevention and Control of COVID-19 — China, 2020
title_short Bibliometric Analysis on Utilization of New Information Technology in the Prevention and Control of COVID-19 — China, 2020
title_sort bibliometric analysis on utilization of new information technology in the prevention and control of covid-19 — china, 2020
topic Methods and Applications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8392999/
https://www.ncbi.nlm.nih.gov/pubmed/34595035
http://dx.doi.org/10.46234/ccdcw2021.047
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