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Big data driven COVID-19 pandemic crisis management: potential approach for global health
INTRODUCTION: Information has the power to protect against unexpected events and control any crisis such as the COVID-19 pandemic. Since COVID-19 has already rapidly spread all over the world, only technology-driven data management can provide accurate information to manage the crisis. This study ai...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Termedia Publishing House
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130465/ https://www.ncbi.nlm.nih.gov/pubmed/34025856 http://dx.doi.org/10.5114/aoms/133522 |
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author | Lv, Yang Ma, Chenwei Li, Xiaohan Wu, Min |
author_facet | Lv, Yang Ma, Chenwei Li, Xiaohan Wu, Min |
author_sort | Lv, Yang |
collection | PubMed |
description | INTRODUCTION: Information has the power to protect against unexpected events and control any crisis such as the COVID-19 pandemic. Since COVID-19 has already rapidly spread all over the world, only technology-driven data management can provide accurate information to manage the crisis. This study aims to explore the potential of big data technologies for controlling COVID-19 transmission and managing it effectively. METHODS: A systematic review guided by PRISMA guidelines has been performed to obtain the key elements. RESULTS: This study identified the thirty-two most relevant documents for qualitative analysis. This study also reveals 10 possible sources and 8 key applications of big data for analyzing the virus infection trend, transmission pattern, virus association, and differences of genetic modifications. It also explores several limitations of big data usage including unethical use, privacy, and exploitative use of data. CONCLUSIONS: The findings of the study will provide new insight and help policymakers and administrators to develop data-driven initiatives to tackle and manage the COVID-19 crisis. |
format | Online Article Text |
id | pubmed-8130465 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Termedia Publishing House |
record_format | MEDLINE/PubMed |
spelling | pubmed-81304652021-05-21 Big data driven COVID-19 pandemic crisis management: potential approach for global health Lv, Yang Ma, Chenwei Li, Xiaohan Wu, Min Arch Med Sci Research Letter INTRODUCTION: Information has the power to protect against unexpected events and control any crisis such as the COVID-19 pandemic. Since COVID-19 has already rapidly spread all over the world, only technology-driven data management can provide accurate information to manage the crisis. This study aims to explore the potential of big data technologies for controlling COVID-19 transmission and managing it effectively. METHODS: A systematic review guided by PRISMA guidelines has been performed to obtain the key elements. RESULTS: This study identified the thirty-two most relevant documents for qualitative analysis. This study also reveals 10 possible sources and 8 key applications of big data for analyzing the virus infection trend, transmission pattern, virus association, and differences of genetic modifications. It also explores several limitations of big data usage including unethical use, privacy, and exploitative use of data. CONCLUSIONS: The findings of the study will provide new insight and help policymakers and administrators to develop data-driven initiatives to tackle and manage the COVID-19 crisis. Termedia Publishing House 2021-03-20 /pmc/articles/PMC8130465/ /pubmed/34025856 http://dx.doi.org/10.5114/aoms/133522 Text en Copyright: © 2020 Termedia & Banach https://creativecommons.org/licenses/by-nc-sa/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License, allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material, provided the original work is properly cited and states its license. |
spellingShingle | Research Letter Lv, Yang Ma, Chenwei Li, Xiaohan Wu, Min Big data driven COVID-19 pandemic crisis management: potential approach for global health |
title | Big data driven COVID-19 pandemic crisis management: potential approach for global health |
title_full | Big data driven COVID-19 pandemic crisis management: potential approach for global health |
title_fullStr | Big data driven COVID-19 pandemic crisis management: potential approach for global health |
title_full_unstemmed | Big data driven COVID-19 pandemic crisis management: potential approach for global health |
title_short | Big data driven COVID-19 pandemic crisis management: potential approach for global health |
title_sort | big data driven covid-19 pandemic crisis management: potential approach for global health |
topic | Research Letter |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130465/ https://www.ncbi.nlm.nih.gov/pubmed/34025856 http://dx.doi.org/10.5114/aoms/133522 |
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