Predicting social distancing index during COVID-19 outbreak through online search engines trends

  1. P. C. Lins-Filho
  2. M. M. S. Araújo
  3. T. S. Macêdo
  4. A. K. A. Ferreira
  5. M. C. F. Melo
  6. E. L. M. S. Silva
  7. J. L. M. Freitas
  8. A. F. Caldas

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Abstract

Online-available information has been considered an accessory tool to estimate epidemiology and collect data on diseases and population behavior patterns. This study aimed to explore the potential use of Google and YouTube relative search volume to predict social distancing index in Brazil during COVID-19 outbreak and verify the correlation between social distancing measures with the course of the epidemic. Data concerning the social distancing index, epidemiological data on COVID-19 in Brazil and the search engines trends for “Coronavirus” were retrieved from online databases. Multiple linear regression was performed and resulted in a statistically significant model evidencing that Google and YouTube relative search volumes are predictors of the social distancing index. The Spearman correlation test revealed a weak correlation between social distancing measures and the course of the COVID-19 epidemic. Health authorities can apply these data to define the proper timing and location for practicing appropriate risk communication strategies.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.28.20115816: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationGoogle Trends (https://trends.google.com/trends/) data is a randomly collected sample of Google search queries, each piece of data is categorized and tagged with a topic.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Google Trends (https://trends.google.com/trends/) data is a randomly collected sample of Google search queries, each piece of data is categorized and tagged with a topic.
    Google
    suggested: (Google, RRID:SCR_017097)
    Data were submitted to statistical analysis, all tests were applied considering an error of 5% and the confidence interval of 95%, and the analyzes were carried out using SPSS software version 23.0 (SPSS Inc. Chicago, IL, USA).
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

  2. Version published to 10.1101/2020.05.28.20115816v1 on medRxiv