Interest in COVID-19 in Latin America and the Caribbean: an infodemiological study using Google Trends

  1. Carlos Jesús Aragón-Ayala
  2. Julissa Copa-Uscamayta
  3. Luis Herrera
  4. Frank Zela-Coila
  5. Cender Udai Quispe-Juli

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Abstract

Infodemiology has been widely used to assess epidemics. In light of the recent pandemic, we use Google Search data to explore online interest about COVID-19 and related topics in 20 countries of Latin America and the Caribbean. Data from Google Trends from December 12, 2019, to April 25, 2020, regarding COVID-19 and other related topics were retrieved and correlated with official data on COVID-19 cases and with national epidemiological indicators. The Latin American and Caribbean countries with the most interest for COVID-19 were Peru (100%) and Panama (98.39%). No correlation was found between this interest and national epidemiological indicators. The global and local response time were 20.2 ± 1.2 days and 16.7 ± 15 days, respectively. The duration of public attention was 64.8 ± 12.5 days. The most popular topics related to COVID-19 were: the country’s situation (100 ± 0) and coronavirus symptoms (36.82 ± 16.16). Most countries showed a strong or moderated (r = 0.72) significant correlation between searches related to COVID-19 and daily new cases. In addition, the highest significant lag correlation was found on day 13.35 ± 5.76 (r = 0.79). Interest shown by Latin American and Caribbean countries for COVID-19 was high. The degree of online interest in a country does not clearly reflect the magnitude of their epidemiological indicators. The response time and the lag correlation were greater than in European and Asian countries. Less interest was found for preventive measures. Strong correlation between searches for COVID-19 and daily new cases suggests a predictive utility.

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  1. Version published to 10.1590/0102-311x00270720
  2. ScreenIT

    SciScore for 10.1101/2020.08.11.20173054: (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
    Normalized data is calculated by dividing the number of searches related to a term by the total of searches done in Google, having previously selected a specific place and time range.
    Google
    suggested: (Google, RRID:SCR_017097)
    Statistical analysis: The GT data was downloaded in a CSV (comma-separated values) format which was then imported into Microsoft Excel 2019.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    Analysis and graphs were done in STATA 14.
    STATA
    suggested: (Stata, RRID:SCR_012763)

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

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: Our study, like other studies using GT, has some limitations. We used only one data source (GT), it could generate a possible selection bias. The study units were the countries; therefore, important subnational factors could not be evaluated. The representativeness of the sample studied cannot be guaranteed due to the heterogeneous internet access in LAC countries. The complexity and dynamism of epidemiological data diminish external validity at the future stages of COVID-19 pandemic. There is a possibility that the selection of the search terms is incomplete due to linguistic complexity and variability, and cultural worldview of each country, despite having carried out a systematic process of identification of terms. In addition, our results cannot be replicated because the algorithm of GT is not publicly accessible and it could change over time.33 4.7. Strengths: Despite our limitations, this is the first study that analyzes and explores the quantitative and qualitative relationship of epidemiological indicators of COVID-19 and the concerns of population on this subject, with the largest number of countries studied until now (20 countries) and from the same geographical region (Latin America and the Caribbean). In addition, we used several terms related to topics regarding COVID-19, having previously verified that they were the most used through multiple tests, unlike studies in China (7), Taiwan,11 among others, that used one or a few terms, and even other stu...

    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 10.1101/2020.08.11.20173054 on medRxiv