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Human behavior in the time of COVID-19: Learning from big data
Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one han...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10118015/ https://www.ncbi.nlm.nih.gov/pubmed/37091459 http://dx.doi.org/10.3389/fdata.2023.1099182 |
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author | Lyu, Hanjia Imtiaz, Arsal Zhao, Yufei Luo, Jiebo |
author_facet | Lyu, Hanjia Imtiaz, Arsal Zhao, Yufei Luo, Jiebo |
author_sort | Lyu, Hanjia |
collection | PubMed |
description | Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the disease. On the other hand, the pandemic has impacted and even changed human behavior in almost every aspect. To provide a holistic understanding of the complex interplay between human behavior and the COVID-19 pandemic, researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning. In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic. In particular, we categorize these studies into three groups—using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities. |
format | Online Article Text |
id | pubmed-10118015 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101180152023-04-21 Human behavior in the time of COVID-19: Learning from big data Lyu, Hanjia Imtiaz, Arsal Zhao, Yufei Luo, Jiebo Front Big Data Big Data Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the disease. On the other hand, the pandemic has impacted and even changed human behavior in almost every aspect. To provide a holistic understanding of the complex interplay between human behavior and the COVID-19 pandemic, researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning. In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic. In particular, we categorize these studies into three groups—using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities. Frontiers Media S.A. 2023-04-06 /pmc/articles/PMC10118015/ /pubmed/37091459 http://dx.doi.org/10.3389/fdata.2023.1099182 Text en Copyright © 2023 Lyu, Imtiaz, Zhao and Luo. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Lyu, Hanjia Imtiaz, Arsal Zhao, Yufei Luo, Jiebo Human behavior in the time of COVID-19: Learning from big data |
title | Human behavior in the time of COVID-19: Learning from big data |
title_full | Human behavior in the time of COVID-19: Learning from big data |
title_fullStr | Human behavior in the time of COVID-19: Learning from big data |
title_full_unstemmed | Human behavior in the time of COVID-19: Learning from big data |
title_short | Human behavior in the time of COVID-19: Learning from big data |
title_sort | human behavior in the time of covid-19: learning from big data |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10118015/ https://www.ncbi.nlm.nih.gov/pubmed/37091459 http://dx.doi.org/10.3389/fdata.2023.1099182 |
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