Associations between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States

  1. Mostafa Abbas
  2. Thomas B. Morland
  3. Eric S. Hall
  4. Yasser EL-Manzalawy

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Abstract

We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19-related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.

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  1. Version published to 10.3390/ijerph18094560
  2. ScreenIT

    SciScore for 10.1101/2021.02.22.21252254: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The dataset includes aggregated state-level daily Google searches for 422 symptoms and conditions that may relate to COVID-19.
    Google
    suggested: (Google, RRID:SCR_017097)
    We evaluated the clustering results for different choices of k (i.e., k = (2,3, …, 10)) using the majority rule applied to 30 different selection criteria (included in NbClust R package (version 3.0) [34]).
    NbClust
    suggested: None

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The present study has some limitations. First, our clustering analyses have identified different patterns from the trajectories of COVID-19 confirmed and death cases in the US from the beginning of the pandemic until the end of October 2020. However, these patterns are dynamic and might change after extending the study time interval. Second, the numbers of daily positive COVID-19 cases reported during the first wave are underestimated due to insufficient testing in most of the states during the first months of the pandemic. Third, our analyses have focused on exploring the associations between Google search trends and COVID-19 spread and mortality trajectories while ignoring other potential factors such as mobility [40, 41] and environmental factors [42, 43] (e.g., temperature and humidity).

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 24 and 21. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2021.02.22.21252254v1 on medRxiv