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大数据分析作为抗击疫情的工具:文献系统综述

Big data analytics as a tool for fighting pandemics: a systematic review of literature.

作者信息

Corsi Alana, de Souza Fabiane Florencio, Pagani Regina Negri, Kovaleski João Luiz

机构信息

Federal University of Technology-Paraná (UTFPR) Câmpus Ponta Grossa, Av. Monteiro Lobato, s/n-Km 04, Ponta Grossa, PR 84016-210 Brazil.

出版信息

J Ambient Intell Humaniz Comput. 2021;12(10):9163-9180. doi: 10.1007/s12652-020-02617-4. Epub 2020 Oct 29.


DOI:10.1007/s12652-020-02617-4
PMID:33144892
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7595572/
Abstract

Infectious and contagious diseases represent a major challenge for health systems worldwide, either in private or public sectors. More recently, with the increase in cases related to these problems, combined with the recent global pandemic of COVID-19, the need to study strategies to treat these health disturbs is even more latent. Big Data, as well as Big Data Analytics techniques, have been addressed in this context with the possibility of predicting, mapping, tracking, monitoring, and raising awareness about these epidemics and pandemics. Thus, the purpose of this study is to identify how BDA can help in cases of pandemics and epidemics. To achieve this purpose, a systematic review of literature was carried out using the methodology Methodi Ordinatio. The rigorous search resulted in a portfolio of 45 articles, retrived from scientific databases. For the collection and analysis of data, the softwares NVivo 12 and VOSviewer were used. The content analysis sought to identify how Big Data and Big Data Analytics can help fighting epidemics and pandemics. The types and sources of data used in cases of previous epidemics and pandemics were identified, as well as techniques for treating these data. The results showed that the main sources of data come from social media and Internet search engines. The most common techniques for analyzing these data involve the use of statistics, such as correlation and regression, combined with other techniques. Results shows that there is a fruitiful field of study to be explored by both areas, Big Data and Health.

摘要

传染病对全球卫生系统来说,无论是在私营部门还是公共部门,都是一项重大挑战。最近,随着与这些问题相关的病例增加,再加上近期的新冠疫情全球大流行,研究治疗这些健康问题的策略的需求变得更加迫切。在这种背景下,大数据以及大数据分析技术被提及,它们有可能对这些流行病和大流行进行预测、绘制地图、追踪、监测并提高人们的认识。因此,本研究的目的是确定大数据分析如何在大流行和流行病的情况下发挥作用。为实现这一目的,使用Methodi Ordinatio方法对文献进行了系统综述。经过严格搜索,从科学数据库中获取了45篇文章。为了收集和分析数据,使用了NVivo 12和VOSviewer软件。内容分析旨在确定大数据和大数据分析如何有助于抗击流行病和大流行。确定了以往流行病和大流行案例中使用的数据类型和来源,以及处理这些数据的技术。结果表明,数据的主要来源来自社交媒体和互联网搜索引擎。分析这些数据最常用的技术包括使用统计方法,如相关性和回归分析,并结合其他技术。结果表明,大数据和健康这两个领域都有一个富有成果的研究领域有待探索。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/cd14157328fd/12652_2020_2617_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/c6e5be49f723/12652_2020_2617_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/35920dad2eae/12652_2020_2617_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/032c6ae5b940/12652_2020_2617_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/7d6914cc9585/12652_2020_2617_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/481ec9519b55/12652_2020_2617_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/310c782c6d59/12652_2020_2617_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/8cc53d74927b/12652_2020_2617_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/4884ba433a44/12652_2020_2617_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/5b5c6aac8fe3/12652_2020_2617_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/3eec95512ab8/12652_2020_2617_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/3e855e82ffeb/12652_2020_2617_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/3940d1666048/12652_2020_2617_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/cd14157328fd/12652_2020_2617_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/c6e5be49f723/12652_2020_2617_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/35920dad2eae/12652_2020_2617_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/032c6ae5b940/12652_2020_2617_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/7d6914cc9585/12652_2020_2617_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/481ec9519b55/12652_2020_2617_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/310c782c6d59/12652_2020_2617_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/8cc53d74927b/12652_2020_2617_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/4884ba433a44/12652_2020_2617_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/5b5c6aac8fe3/12652_2020_2617_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/3eec95512ab8/12652_2020_2617_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/3e855e82ffeb/12652_2020_2617_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/3940d1666048/12652_2020_2617_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/539f/7595572/cd14157328fd/12652_2020_2617_Fig13_HTML.jpg

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