Prediction of COVID-19 Outbreaks Using Google Trends in India: A Retrospective Analysis.
Open accessAbstract
OBJECTIVE: Considering the rising menace of coronavirus disease 2019 (COVID-19), it is essential to explore the methods and resources that might predict the case numbers expected and identify the locations of outbreaks. Hence, we have done the following study to explore the potential use of Google Trends (GT) in predicting the COVID-19 outbreak in India. METHODS: The Google search terms used for the analysis were "coronavirus", "COVID", "COVID 19", "corona", and "virus". GTs for these terms in Google Web, News, and YouTube, and the data on COVID-19 case numbers were obtained. Spearman correlation and lag correlation were used to determine the correlation between COVID-19 cases and the Google search terms. RESULTS: "Coronavirus" and "corona" were the terms most commonly used by Internet surfers in India. Correlation for the GTs of the search terms "coronavirus" and "corona" was high (r > 0.7) with the daily cumulative and new COVID-19 cases for a lag period ranging from 9 to 21 days. The maximum lag period for predicting COVID-19 cases was found to be with the News search for the term "coronavirus", with 21 days, i.e., the search volume for "coronavirus" peaked 21 days before the peak number of cases reported by the disease surveillance system. CONCLUSION: Our study revealed that GTs may predict outbreaks of COVID-19, 2 to 3 weeks earlier than the routine disease surveillance, in India. Google search data may be considered as a supplementary tool in COVID-19 monitoring and planning in India.
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Growing interest in the current COVID-19 pandemic and online information search about COVID-19 were reported in many countries in Asia, Europe, and the United States.
“Similarly, the growing interest in the current COVID-19 pandemic and online information search about COVID-19 were reported in many countries in Asia, Europe, and the United States [16, 45–48].”
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Htay, M. N. N., Parial, L. L., Tolabing, M. C., Dadaczynski, K., Okan, O., Leung, A. Y. M., & Su, T. T. (2022) · PLOS ONE · doi:10.1371/journal.pone.0266276 · Cited by 78 · see citing works
Changes to public health surveillance methods due to the COVID-19 pandemic: scoping review
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- Authors
- Venkatesh, U., & Gandhi, P. A.
- Affiliations
- Safdarjang Hospital (IN)
- Venue
- Healthcare Informatics Research
- Published
- 2020-07-31
- Volume
- 26
- Issue
- 3
- Pages
- 175-184
- ISSN
- 2093-3681 (ISSN-L)
- 2093-369X
- PMC
- PMC7438693
- OpenAlex
- W3047626285
- Cited by
- 62
- References
- 1
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 5 · 2023: 10 · 2022: 19 · 2021: 18 · 2020: 2
- FWCI
- 8.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Healthcare Informatics Research (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- COVID-19 pandemic, Google Trends, epidemic forecasting, search volume, lag correlation, disease surveillance, India, public health monitoring, internet search data
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