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Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: – evidence from Baidu index
BACKGROUND: New coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19. METHODS: We collected the...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7819631/ https://www.ncbi.nlm.nih.gov/pubmed/33478425 http://dx.doi.org/10.1186/s12879-020-05740-x |
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author | Tu, Bizhi Wei, Laifu Jia, Yaya Qian, Jun |
author_facet | Tu, Bizhi Wei, Laifu Jia, Yaya Qian, Jun |
author_sort | Tu, Bizhi |
collection | PubMed |
description | BACKGROUND: New coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19. METHODS: We collected the number of COVID-19 confirmed cases between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from the Baidu Index. Spearman’s correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed. RESULTS: Daily growth of confirmed cases and Baidu index values for each COVID-19-related symptom presented robust positive correlations during the outbreak (fever: r(s)=0.705, p=9.623× 10(− 6); cough: r(s)=0.592, p=4.485× 10(− 4); fatigue: r(s)=0.629, p=1.494× 10(− 4); sputum production: r(s)=0.648, p=8.206× 10(− 5); shortness of breath: r(s)=0.656, p=6.182× 10(–5)). The average search-to-confirmed interval (STCI) was 19.8 days in China. The daily Baidu Index value’s optimal time lags were the 4 days for cough, 2 days for fatigue, 3 days for sputum production, 1 day for shortness of breath, and 0 days for fever. CONCLUSION: The searches of COVID-19-related symptoms on the Baidu search engine were significantly correlated to the number of confirmed cases. Since the Baidu search engine could reflect the public’s attention to the pandemic and the regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-020-05740-x. |
format | Online Article Text |
id | pubmed-7819631 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-78196312021-01-22 Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: – evidence from Baidu index Tu, Bizhi Wei, Laifu Jia, Yaya Qian, Jun BMC Infect Dis Research Article BACKGROUND: New coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19. METHODS: We collected the number of COVID-19 confirmed cases between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from the Baidu Index. Spearman’s correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed. RESULTS: Daily growth of confirmed cases and Baidu index values for each COVID-19-related symptom presented robust positive correlations during the outbreak (fever: r(s)=0.705, p=9.623× 10(− 6); cough: r(s)=0.592, p=4.485× 10(− 4); fatigue: r(s)=0.629, p=1.494× 10(− 4); sputum production: r(s)=0.648, p=8.206× 10(− 5); shortness of breath: r(s)=0.656, p=6.182× 10(–5)). The average search-to-confirmed interval (STCI) was 19.8 days in China. The daily Baidu Index value’s optimal time lags were the 4 days for cough, 2 days for fatigue, 3 days for sputum production, 1 day for shortness of breath, and 0 days for fever. CONCLUSION: The searches of COVID-19-related symptoms on the Baidu search engine were significantly correlated to the number of confirmed cases. Since the Baidu search engine could reflect the public’s attention to the pandemic and the regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-020-05740-x. BioMed Central 2021-01-21 /pmc/articles/PMC7819631/ /pubmed/33478425 http://dx.doi.org/10.1186/s12879-020-05740-x Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Tu, Bizhi Wei, Laifu Jia, Yaya Qian, Jun Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: – evidence from Baidu index |
title | Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: – evidence from Baidu index |
title_full | Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: – evidence from Baidu index |
title_fullStr | Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: – evidence from Baidu index |
title_full_unstemmed | Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: – evidence from Baidu index |
title_short | Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: – evidence from Baidu index |
title_sort | using baidu search values to monitor and predict the confirmed cases of covid-19 in china: – evidence from baidu index |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7819631/ https://www.ncbi.nlm.nih.gov/pubmed/33478425 http://dx.doi.org/10.1186/s12879-020-05740-x |
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