Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020

The peak of Internet searches and social media data about the coronavirus disease 2019 (COVID-19) outbreak occurred 10–14 days earlier than the peak of daily incidences in China. Internet searches and social media data had high correlation with daily incidences, with the maximum r > 0.89 in all c...

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Detalles Bibliográficos
Autores principales: Li, Cuilian, Chen, Li Jia, Chen, Xueyu, Zhang, Mingzhi, Pang, Chi Pui, Chen, Haoyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: European Centre for Disease Prevention and Control (ECDC) 2020
Materias:
Rapid Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7078825/
https://www.ncbi.nlm.nih.gov/pubmed/32183935
http://dx.doi.org/10.2807/1560-7917.ES.2020.25.10.2000199
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author Li, Cuilian
Chen, Li Jia
Chen, Xueyu
Zhang, Mingzhi
Pang, Chi Pui
Chen, Haoyu
author_facet Li, Cuilian
Chen, Li Jia
Chen, Xueyu
Zhang, Mingzhi
Pang, Chi Pui
Chen, Haoyu
author_sort Li, Cuilian
collection PubMed
description The peak of Internet searches and social media data about the coronavirus disease 2019 (COVID-19) outbreak occurred 10–14 days earlier than the peak of daily incidences in China. Internet searches and social media data had high correlation with daily incidences, with the maximum r > 0.89 in all correlations. The lag correlations also showed a maximum correlation at 8–12 days for laboratory-confirmed cases and 6–8 days for suspected cases.
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spelling pubmed-70788252020-03-23 Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020 Li, Cuilian Chen, Li Jia Chen, Xueyu Zhang, Mingzhi Pang, Chi Pui Chen, Haoyu Euro Surveill Rapid Communication The peak of Internet searches and social media data about the coronavirus disease 2019 (COVID-19) outbreak occurred 10–14 days earlier than the peak of daily incidences in China. Internet searches and social media data had high correlation with daily incidences, with the maximum r > 0.89 in all correlations. The lag correlations also showed a maximum correlation at 8–12 days for laboratory-confirmed cases and 6–8 days for suspected cases. European Centre for Disease Prevention and Control (ECDC) 2020-03-12 /pmc/articles/PMC7078825/ /pubmed/32183935 http://dx.doi.org/10.2807/1560-7917.ES.2020.25.10.2000199 Text en This article is copyright of the authors or their affiliated institutions, 2020. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0) Licence. You may share and adapt the material, but must give appropriate credit to the source, provide a link to the licence, and indicate if changes were made.
spellingShingle Rapid Communication
Li, Cuilian
Chen, Li Jia
Chen, Xueyu
Zhang, Mingzhi
Pang, Chi Pui
Chen, Haoyu
Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020
title Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020
title_full Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020
title_fullStr Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020
title_full_unstemmed Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020
title_short Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020
title_sort retrospective analysis of the possibility of predicting the covid-19 outbreak from internet searches and social media data, china, 2020
topic Rapid Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7078825/
https://www.ncbi.nlm.nih.gov/pubmed/32183935
http://dx.doi.org/10.2807/1560-7917.ES.2020.25.10.2000199
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