Prediction of COVID-19 Outbreaks Using Google Trends in India: A Retrospective Analysis
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Objectives: Considering the rising menace of coronavirus disease 2019 (COVID-19), it is essential to explore the methodsand resources that might predict the case numbers expected and identify the locations of outbreaks. Hence, we have done thefollowing 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 theseterms in Google Web, News, and YouTube, and the data on COVID-19 case numbers were obtained. Spearman correlationand 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 ofthe search terms “coronavirus” and “corona” was high (r > 0.7) with the daily cumulative and new COVID-19 cases for a lagperiod ranging from 9 to 21 days. The maximum lag period for predicting COVID-19 cases was found to be with the Newssearch for the term “coronavirus”, with 21 days, i.e., the search volume for “coronavirus” peaked 21 days before the peak numberof cases reported by the disease surveillance system. Conclusions: Our study revealed that GTs may predict outbreaks ofCOVID-19, 2 to 3 weeks earlier than the routine disease surveillance, in India. Google search data may be considered as asupplementary tool in COVID-19 monitoring and planning in India.
Abstract
Objectives: Considering the rising menace of coronavirus disease 2019 (COVID-19), it is essential to explore the methodsand resources that might predict the case numbers expected and identify the locations of outbreaks. Hence, we have done thefollowing 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 theseterms in Google Web, News, and YouTube, and the data on COVID-19 case numbers were obtained. Spearman correlationand 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 ofthe search terms “coronavirus” and “corona” was high (r > 0.7) with the daily cumulative and new COVID-19 cases for a lagperiod ranging from 9 to 21 days. The maximum lag period for predicting COVID-19 cases was found to be with the Newssearch for the term “coronavirus”, with 21 days, i.e., the search volume for “coronavirus” peaked 21 days before the peak numberof cases reported by the disease surveillance system. Conclusions: Our study revealed that GTs may predict outbreaks ofCOVID-19, 2 to 3 weeks earlier than the routine disease surveillance, in India. Google search data may be considered as asupplementary tool in COVID-19 monitoring and planning in India.
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