Characterization of the Second Wave of the COVID-19 Pandemic in India: A Google Trends Analysis

  1. Aayush Visaria
  2. Pooja Polamarasetti
  3. Shivani Reddy
  4. Alizah Ali
  5. Fariha R. Hameed
  6. Joel James
  7. Moizz Akhtar
  8. Sumaiya Islam
  9. Priyanka Raju
  10. Rajat Thawani

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Background

The second wave of the COVID-19 pandemic has led to considerable morbidity and mortality in India, in part due to lack of healthcare access, low health literacy, and poor disease surveillance. In this retrospective, descriptive ecological study, we utilized Google Trends (GT) to characterize the second COVID-19 wave and its association with official case counts based on search terms related to symptoms, testing, disease complications, medications, preventive behaviors, and healthcare utilization.

Methods

GT is a publicly available, online tracking system of Google searches. Searches are presented as relative search volumes (RSV) from 0 (least) to 100 (most number of searches). We performed pre-defined Web searches in India from 2/12/2021 to 5/09/2021. We characterized the peak RSV, RSV doubling rates, and Spearman rank correlation of selected search terms with official case counts. We also used date-adjusted linear regression to estimate the association between highly correlated search terms and official case counts. We then qualitatively classified public search queries into thematic groups to better understand public awareness and needs related to COVID-19.

Results

We observed that searches for symptoms (most searched terms in order: fever, cough, headache, fatigue, chest pain), disease states (infection, pneumonia), COVID-19-related medications (remdesivir, ivermectin, azithromycin, Fabiflu, dexamethasone), testing modalities (PCR, CT Scan, D-dimer, C-reactive protein, oxygen saturation), healthcare utilization (oxygen cylinders, hospital, physician), and preventive behaviors (lockdown, mask, pulse oximetry, hand sanitizer, quarantine) all demonstrated increases, in line with increases in official case counts. Symptoms, PCR testing, outpatient medications, and preventive behaviors peaked around April 24th, approximately two weeks prior to the peak RSV in official case counts. Contrarily, healthcare utilization factors, including searches for hospital, physicians, beds, disease states, and inpatient medications did not peak until the first week of May. There were highly significant correlations between ‘Coronavirus Disease 2019’ (r=0.959), ‘fever’ (r=0.935), ‘pulse oximetry’ (r=0.952), ‘oxygen saturation’ (r=0.944), ‘C-reactive protein’ (r=0.955), ‘D-Dimer’ (r=0.945), & ‘Fabiflu’ (r=0.943) and official case counts.

Conclusion

GT search terms related to symptoms, testing, and medications are highly correlated with official case counts in India, suggesting need for further studies examining GT’s potential use as a disease surveillance and public informant tool for public health officials.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.05.19.21257473: (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
    Data source: We utilized GT, a publicly available, online tracking system of Google searches by search terms, topics, geographic region, and date.
    Google
    suggested: (Google, RRID:SCR_017097)

    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 results should be interpreted with caution due to many limitations. Firstly, our ecological study design does not allow us to make inferences at the individual patient level, nor does it provide information on the direction of correlation. Increases in searches may, at least partially, be due to increased presence of related topics in the media rather than individual situations. For example, Remdesivir exports were prohibited by Indian government officials on April 11th in anticipation of the predicted spike in demand for the drug. This may have contributed to the rise in interest for this term earlier than other medications. Furthermore, searches for symptoms may not be solely due to COVID-19. However, given the high prevalence of COVID-19 In India and the significant correlation with confirmed COVID-19 cases, it is reasonable to infer that rises in related search terms are due to COVID-19. Additionally, search terms may not capture all non-English languages and regional colloquialisms. Although we tried to choose terms that would capture the largest percentage of related terms, this may have decreased specificity of searches. For example, the search topic “mask” rather than “face mask” or “covid mask” may encompass all of the intended searches with a high sensitivity; although the decision to choose the more general search term decreases specificity when including searches intended to find other masks, such as those for skincare or fashion purposes. Despite these limita...

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.05.19.21257473v1 on medRxiv