Suitability of Google Trends™ for Digital Surveillance During Ongoing COVID-19 Epidemic: A Case Study from India

  1. Parmeshwar Satpathy
  2. Sanjeev Kumar
  3. Pankaj Prasad

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Abstract

Objective:

Digital surveillance has shown mixed results as a supplement to traditional surveillance. Google Trends™ (GT) (Google, Mountain View, CA, United States) has been used for digital surveillance of H1N1, Ebola and MERS. We used GT to correlate the information seeking on COVID-19 with number of tests and cases in India.

Methods:

Data was obtained on daily tests and cases from WHO, ECDC and covid19india.org. We used a comprehensive search strategy to retrieve GT data on COVID-19 related information-seeking behavior in India between January 1 and May 31, 2020 in the form of relative search volume (RSV). We also used time-lag correlation analysis to assess the temporal relationships between RSV and daily new COVID-19 cases and tests.

Results:

GT RSV showed high time-lag correlation with both daily reported tests and cases for the terms “COVID 19,” “COVID,” “social distancing,” “soap,” and “lockdown” at the national level. In 5 high-burden states, high correlation was observed for these 5 terms along with “Corona.” Peaks in RSV, both at the national level and in high-burden states corresponded with media coverage or government declarations on the ongoing pandemic.

Conclusion:

The correlation observed between GT data and COVID-19 tests/cases in India may be either due to media-coverage-induced curiosity, or health-seeking curiosity.

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  1. Version published to 10.1017/dmp.2021.249
  2. ScreenIT

    SciScore for 10.1101/2020.08.24.20176321: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We filtered the Relative search volume (RSV) data both at national level as well as by geographic regions (states and Union Territories) of India.
    Relative
    suggested: (RELATIVE, RRID:SCR_009355)
    We used Pearson’s correlation analysis to examine the correlations of RSV data of Google search terms with daily tests conducted and daily new laboratory confirmed COVID-19 cases separately.
    Google
    suggested: (Google, RRID:SCR_017097)
    We used the advanced data analysis tools available in Microsoft Excel 365 for this.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    We hypothesize that lack of such confirmatory studies led to demolishing of Google Flu search network.(66) Our study does have certain limitations. The selection of the keyword as well as the associated spelling might affect the overall results of the study. There is no globally accepted guideline for Google Trends™ analysis, though a procedure has been recommended by some authors.(61) A guideline is required from Google™ in this regard who has custody of the search data, but doesn’t share the algorithm for search.(67)

    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: We did not find any issues relating to colormaps.

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.08.24.20176321v1 on medRxiv