Google Searches for Taste and Smell Loss Anticipate Covid-19 Epidemiology

  1. Giuseppe Lippi
  2. Brandon M. Henry
  3. Camilla Mattiuzzi
  4. Fabian Sanchis-Gomar

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Abstract

Background

As evidence emerged that loss of taste and/or loss of smell is frequently triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, we investigated whether Google searches volume for these two disease-specific symptoms could be associated with disease epidemiology in United States (US).

Materials and Methods

We performed an electronic search in Google Trends using the keywords “taste loss” and “smell loss” within the US. The Google searches volume was correlated with the number of new weekly cases of coronavirus disease 2019 (COVID-19) in the country.

Results

The weekly Google searches for taste and smell loss exhibited a trend similar to that of new weekly SARS-CoV-2 infections in the US. A nearly perfect correlation was found between Google Trends scores of taste and smell loss (r=0.98; 95% CI, 0.97-0.99; p<0.001). Although a significant association was found between Google searches for the two symptoms and the concomitant number of new weekly SARS-CoV-2 infections reported during the same week, the correlation improved over time. The highest correlation was found comparing Google Trends scores for taste or smell loss and the number of new weekly SARS-CoV-2 infections two weeks later. The correlation coefficient of summing Google Trends scores for the two symptoms and the number of new weekly SARS-CoV-2 infections two weeks later was 0.82 (95% CI, 0.68-0.90; p<0.001), and was associated 0.89 diagnostic accuracy.

Conclusions

These findings suggest that Google searches numbers for olfactory and gustatory dysfunctions may help predicting the epidemiological trajectory of COVID-19 early before official reporting.

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  1. ScreenIT

    SciScore for 10.1101/2020.11.09.20228510: (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
    The weekly Google Trends score, reflecting the mean volume of Google searches for a specific term in the preceding seven days, was downloaded and imported into a Microsoft Excel file
    Google
    suggested: (Google, RRID:SCR_017097)
    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: 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

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  2. Version published to 10.1101/2020.11.09.20228510v1 on medRxiv