The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis.

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  • Additional Information
    • Source:
      Publisher: JMIR Publications Country of Publication: Canada NLM ID: 101669345 Publication Model: Electronic Cited Medium: Internet ISSN: 2369-2960 (Electronic) Linking ISSN: 23692960 NLM ISO Abbreviation: JMIR Public Health Surveill Subsets: MEDLINE
    • Publication Information:
      Original Publication: Toronto : JMIR Publications, [2015]-
    • Subject Terms:
      COVID-19* ; Causality* ; Data Interpretation, Statistical* ; Public Health Surveillance*; Coronavirus Infections/*epidemiology ; Restaurants/*statistics & numerical data ; Search Engine/*trends; Adult ; Data Mining ; Humans ; United States/epidemiology
    • Abstract:
      Background: The COVID-19 pandemic has affected virtually every region in the world. At the time of this study, the number of daily new cases in the United States was greater than that in any other country, and the trend was increasing in most states. Google Trends provides data regarding public interest in various topics during different periods. Analyzing these trends using data mining methods may provide useful insights and observations regarding the COVID-19 outbreak.
      Objective: The objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regard to the increase of daily cases in the United States. In particular, we are concerned with searches related to dine-in restaurants and bars. Data were obtained from the Google Trends application programming interface and the COVID-19 Tracking Project.
      Methods: To test the causation of one time series on another, we used the Granger causality test. We considered the causation of two different search query trends related to dine-in restaurants and bars on daily positive cases in the US states and territories with the 10 highest and 10 lowest numbers of daily new cases of COVID-19. In addition, we used Pearson correlations to measure the linear relationships between different trends.
      Results: Our results showed that for states and territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly occurred after reopening, significantly affected the number of daily new cases on average. California, for example, showed the most searches for restaurants on June 7, 2020; this affected the number of new cases within two weeks after the peak, with a P value of .004 for the Granger causality test.
      Conclusions: Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases in US states and territories with higher numbers of daily new cases. We showed that these influential search trends can be used to provide additional information for prediction tasks regarding new cases in each region. These predictions can help health care leaders manage and control the impact of the COVID-19 outbreak on society and prepare for its outcomes.
      (©Milad Asgari Mehrabadi, Nikil Dutt, Amir M Rahmani. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 06.04.2021.)
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    • Contributed Indexing:
      Keywords: COVID-19; Google Trends; LSTM; bars; coronavirus; deep learning; infodemiology; infoveillance; machine learning; restaurants
    • Publication Date:
      Date Created: 20210310 Date Completed: 20210426 Latest Revision: 20231111
    • Publication Date:
      20231111
    • Accession Number:
      PMC8025919
    • Accession Number:
      10.2196/22880
    • Accession Number:
      33690143