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

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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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          Most cited references48

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          Detecting influenza epidemics using search engine query data.

          Jeremy Ginsberg, Matthew H. Mohebbi, Rajan S. Patel … (2009)
          Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year. In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza. One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users.
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            Big data. The parable of Google Flu: traps in big data analysis.

            David Lazer, Ryan Kennedy, Gary King … (2014)
            Article 0 comments Cited 256 times – based on 0 reviews     Review now
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              The Use of Google Trends in Health Care Research: A Systematic Review

              Sudhakar V. Nuti, Brian Wayda, Isuru Ranasinghe … (2014)
              Background Google Trends is a novel, freely accessible tool that allows users to interact with Internet search data, which may provide deep insights into population behavior and health-related phenomena. However, there is limited knowledge about its potential uses and limitations. We therefore systematically reviewed health care literature using Google Trends to classify articles by topic and study aim; evaluate the methodology and validation of the tool; and address limitations for its use in research. Methods and Findings PRISMA guidelines were followed. Two independent reviewers systematically identified studies utilizing Google Trends for health care research from MEDLINE and PubMed. Seventy studies met our inclusion criteria. Google Trends publications increased seven-fold from 2009 to 2013. Studies were classified into four topic domains: infectious disease (27% of articles), mental health and substance use (24%), other non-communicable diseases (16%), and general population behavior (33%). By use, 27% of articles utilized Google Trends for casual inference, 39% for description, and 34% for surveillance. Among surveillance studies, 92% were validated against a reference standard data source, and 80% of studies using correlation had a correlation statistic ≥0.70. Overall, 67% of articles provided a rationale for their search input. However, only 7% of articles were reproducible based on complete documentation of search strategy. We present a checklist to facilitate appropriate methodological documentation for future studies. A limitation of the study is the challenge of classifying heterogeneous studies utilizing a novel data source. Conclusion Google Trends is being used to study health phenomena in a variety of topic domains in myriad ways. However, poor documentation of methods precludes the reproducibility of the findings. Such documentation would enable other researchers to determine the consistency of results provided by Google Trends for a well-specified query over time. Furthermore, greater transparency can improve its reliability as a research tool.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Disaster Med Public Health Prep
                Journal ID (iso-abbrev): Disaster Med Public Health Prep
                Journal ID (publisher-id): DMP
                Title: Disaster Medicine and Public Health Preparedness
                Publisher: Cambridge University Press (New York, USA )
                ISSN (Print): 1935-7893
                ISSN (Electronic): 1938-744X
                Publication date (Electronic, pub): 03 August 2021
                Pages: 1-10
                Affiliations
                [ 1 ]Department of Community Medicine, Veer Surendra Sai Institute of Medical Sciences and Research , Burla, Odisha, India
                [ 2 ]Department of Community and Family Medicine, All India Institute of Medical Sciences (AIIMS) , Bhopal, Madhya Pradesh, India
                Author notes
                Corresponding author: Sanjeev Kumar, Email: docsanjiv@ 123456gmail.com .
                Author information
                https://orcid.org/0000-0001-7106-7606
                https://orcid.org/0000-0003-2730-2378
                Article
                Publisher-manuscript ID: DMPHP-20-2898 Publisher Item ID: S1935789321002494
                DOI: 10.1017/dmp.2021.249
                PMC ID: 8460424
                PubMed ID: 34343467
                SO-VID: f49bcf4d-8828-41b5-9fed-ac4d5994529a
                Copyright © © Society for Disaster Medicine and Public Health, Inc. 2021
                License:

                This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 31 December 2020
                Date revision received : 03 May 2021
                Date accepted : 24 July 2021
                Page count
                Figures: 4, Tables: 3, References: 59, Pages: 10
                Categories
                Subject: Original Research

                Keywords: disease surveillance,infodemiology,ict in healthcare,pandemic,time lag correlation
                Data availability:
                Keywords: disease surveillance, infodemiology, ict in healthcare, pandemic, time lag correlation

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