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Correlations Between COVID-19 Cases and Google Trends Data in the United States: A State-by-State Analysis
OBJECTIVE: To evaluate whether a digital surveillance model using Google Trends is feasible for obtaining accurate data on coronavirus disease 2019 and whether accurate predictions can be made regarding new cases. METHODS: Data on total and daily new cases in each US state were collected from Januar...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Mayo Foundation for Medical Education and Research
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7439962/ https://www.ncbi.nlm.nih.gov/pubmed/33164756 http://dx.doi.org/10.1016/j.mayocp.2020.08.022 |
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author | Kurian, Shyam J. Bhatti, Atiq ur Rehman Alvi, Mohammed Ali Ting, Henry H. Storlie, Curtis Wilson, Patrick M. Shah, Nilay D. Liu, Hongfang Bydon, Mohamad |
author_facet | Kurian, Shyam J. Bhatti, Atiq ur Rehman Alvi, Mohammed Ali Ting, Henry H. Storlie, Curtis Wilson, Patrick M. Shah, Nilay D. Liu, Hongfang Bydon, Mohamad |
author_sort | Kurian, Shyam J. |
collection | PubMed |
description | OBJECTIVE: To evaluate whether a digital surveillance model using Google Trends is feasible for obtaining accurate data on coronavirus disease 2019 and whether accurate predictions can be made regarding new cases. METHODS: Data on total and daily new cases in each US state were collected from January 22, 2020, to April 6, 2020. Information regarding 10 keywords was collected from Google Trends, and correlation analyses were performed for individual states as well as for the United States overall. RESULTS: Among the 10 keywords analyzed from Google Trends, face mask, Lysol, and COVID stimulus check had the strongest correlations when looking at the United States as a whole, with R values of 0.88, 0.82, and 0.79, respectively. Lag and lead Pearson correlations were assessed for every state and all 10 keywords from 16 days before the first case in each state to 16 days after the first case. Strong correlations were seen up to 16 days prior to the first reported cases in some states. CONCLUSION: This study documents the feasibility of syndromic surveillance of internet search terms to monitor new infectious diseases such as coronavirus disease 2019. This information could enable better preparation and planning of health care systems. |
format | Online Article Text |
id | pubmed-7439962 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Mayo Foundation for Medical Education and Research |
record_format | MEDLINE/PubMed |
spelling | pubmed-74399622020-08-21 Correlations Between COVID-19 Cases and Google Trends Data in the United States: A State-by-State Analysis Kurian, Shyam J. Bhatti, Atiq ur Rehman Alvi, Mohammed Ali Ting, Henry H. Storlie, Curtis Wilson, Patrick M. Shah, Nilay D. Liu, Hongfang Bydon, Mohamad Mayo Clin Proc Original Article OBJECTIVE: To evaluate whether a digital surveillance model using Google Trends is feasible for obtaining accurate data on coronavirus disease 2019 and whether accurate predictions can be made regarding new cases. METHODS: Data on total and daily new cases in each US state were collected from January 22, 2020, to April 6, 2020. Information regarding 10 keywords was collected from Google Trends, and correlation analyses were performed for individual states as well as for the United States overall. RESULTS: Among the 10 keywords analyzed from Google Trends, face mask, Lysol, and COVID stimulus check had the strongest correlations when looking at the United States as a whole, with R values of 0.88, 0.82, and 0.79, respectively. Lag and lead Pearson correlations were assessed for every state and all 10 keywords from 16 days before the first case in each state to 16 days after the first case. Strong correlations were seen up to 16 days prior to the first reported cases in some states. CONCLUSION: This study documents the feasibility of syndromic surveillance of internet search terms to monitor new infectious diseases such as coronavirus disease 2019. This information could enable better preparation and planning of health care systems. Mayo Foundation for Medical Education and Research 2020-11 2020-08-20 /pmc/articles/PMC7439962/ /pubmed/33164756 http://dx.doi.org/10.1016/j.mayocp.2020.08.022 Text en © 2020 Mayo Foundation for Medical Education and Research. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Original Article Kurian, Shyam J. Bhatti, Atiq ur Rehman Alvi, Mohammed Ali Ting, Henry H. Storlie, Curtis Wilson, Patrick M. Shah, Nilay D. Liu, Hongfang Bydon, Mohamad Correlations Between COVID-19 Cases and Google Trends Data in the United States: A State-by-State Analysis |
title | Correlations Between COVID-19 Cases and Google Trends Data in the United States: A State-by-State Analysis |
title_full | Correlations Between COVID-19 Cases and Google Trends Data in the United States: A State-by-State Analysis |
title_fullStr | Correlations Between COVID-19 Cases and Google Trends Data in the United States: A State-by-State Analysis |
title_full_unstemmed | Correlations Between COVID-19 Cases and Google Trends Data in the United States: A State-by-State Analysis |
title_short | Correlations Between COVID-19 Cases and Google Trends Data in the United States: A State-by-State Analysis |
title_sort | correlations between covid-19 cases and google trends data in the united states: a state-by-state analysis |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7439962/ https://www.ncbi.nlm.nih.gov/pubmed/33164756 http://dx.doi.org/10.1016/j.mayocp.2020.08.022 |
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