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United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study

BACKGROUND: The emergence and media coverage of COVID-19 may have affected influenza search patterns, possibly affecting influenza surveillance results using Google Trends. OBJECTIVE: We aimed to investigate if the emergence of COVID-19 was associated with modifications in influenza search patterns...

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Detalles Bibliográficos
Autores principales: Cai, Owen, Sousa-Pinto, Bernardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8896565/
https://www.ncbi.nlm.nih.gov/pubmed/34878996
http://dx.doi.org/10.2196/32364
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author Cai, Owen
Sousa-Pinto, Bernardo
author_facet Cai, Owen
Sousa-Pinto, Bernardo
author_sort Cai, Owen
collection PubMed
description BACKGROUND: The emergence and media coverage of COVID-19 may have affected influenza search patterns, possibly affecting influenza surveillance results using Google Trends. OBJECTIVE: We aimed to investigate if the emergence of COVID-19 was associated with modifications in influenza search patterns in the United States. METHODS: We retrieved US Google Trends data (relative number of searches for specified terms) for the topics influenza, Coronavirus disease 2019, and symptoms shared between influenza and COVID-19. We calculated the correlations between influenza and COVID-19 search data for a 1-year period after the first COVID-19 diagnosis in the United States (January 21, 2020 to January 20, 2021). We constructed a seasonal autoregressive integrated moving average model and compared predicted search volumes, using the 4 previous years, with Google Trends relative search volume data. We built a similar model for shared symptoms data. We also assessed correlations for the past 5 years between Google Trends influenza data, US Centers for Diseases Control and Prevention influenza-like illness data, and influenza media coverage data. RESULTS: We observed a nonsignificant weak correlation (ρ= –0.171; P=0.23) between COVID-19 and influenza Google Trends data. Influenza search volumes for 2020-2021 distinctly deviated from values predicted by seasonal autoregressive integrated moving average models—for 6 weeks within the first 13 weeks after the first COVID-19 infection was confirmed in the United States, the observed volume of searches was higher than the upper bound of 95% confidence intervals for predicted values. Similar results were observed for shared symptoms with influenza and COVID-19 data. The correlation between Google Trends influenza data and CDC influenza-like-illness data decreased after the emergence of COVID-19 (2020-2021: ρ=0.643; 2019-2020: ρ=0.902), while the correlation between Google Trends influenza data and influenza media coverage volume remained stable (2020-2021: ρ=0.746; 2019-2020: ρ=0.707). CONCLUSIONS: Relevant differences were observed between predicted and observed influenza Google Trends data the year after the onset of the COVID-19 pandemic in the United States. Such differences are possibly due to media coverage, suggesting limitations to the use of Google Trends as a flu surveillance tool.
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spelling pubmed-88965652022-03-10 United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study Cai, Owen Sousa-Pinto, Bernardo JMIR Public Health Surveill Original Paper BACKGROUND: The emergence and media coverage of COVID-19 may have affected influenza search patterns, possibly affecting influenza surveillance results using Google Trends. OBJECTIVE: We aimed to investigate if the emergence of COVID-19 was associated with modifications in influenza search patterns in the United States. METHODS: We retrieved US Google Trends data (relative number of searches for specified terms) for the topics influenza, Coronavirus disease 2019, and symptoms shared between influenza and COVID-19. We calculated the correlations between influenza and COVID-19 search data for a 1-year period after the first COVID-19 diagnosis in the United States (January 21, 2020 to January 20, 2021). We constructed a seasonal autoregressive integrated moving average model and compared predicted search volumes, using the 4 previous years, with Google Trends relative search volume data. We built a similar model for shared symptoms data. We also assessed correlations for the past 5 years between Google Trends influenza data, US Centers for Diseases Control and Prevention influenza-like illness data, and influenza media coverage data. RESULTS: We observed a nonsignificant weak correlation (ρ= –0.171; P=0.23) between COVID-19 and influenza Google Trends data. Influenza search volumes for 2020-2021 distinctly deviated from values predicted by seasonal autoregressive integrated moving average models—for 6 weeks within the first 13 weeks after the first COVID-19 infection was confirmed in the United States, the observed volume of searches was higher than the upper bound of 95% confidence intervals for predicted values. Similar results were observed for shared symptoms with influenza and COVID-19 data. The correlation between Google Trends influenza data and CDC influenza-like-illness data decreased after the emergence of COVID-19 (2020-2021: ρ=0.643; 2019-2020: ρ=0.902), while the correlation between Google Trends influenza data and influenza media coverage volume remained stable (2020-2021: ρ=0.746; 2019-2020: ρ=0.707). CONCLUSIONS: Relevant differences were observed between predicted and observed influenza Google Trends data the year after the onset of the COVID-19 pandemic in the United States. Such differences are possibly due to media coverage, suggesting limitations to the use of Google Trends as a flu surveillance tool. JMIR Publications 2022-03-03 /pmc/articles/PMC8896565/ /pubmed/34878996 http://dx.doi.org/10.2196/32364 Text en ©Owen Cai, Bernardo Sousa-Pinto. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 03.03.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Cai, Owen
Sousa-Pinto, Bernardo
United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study
title United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study
title_full United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study
title_fullStr United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study
title_full_unstemmed United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study
title_short United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study
title_sort united states influenza search patterns since the emergence of covid-19: infodemiology study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8896565/
https://www.ncbi.nlm.nih.gov/pubmed/34878996
http://dx.doi.org/10.2196/32364
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