Analyzing Cyberchondriac Google Trends Data to Forecast Waves and Avoid Friction: Lessons From COVID-19 in India

Abstract

This article examines the Google Trends data related to the second COVID-19 wave in India. We investigate the phenomenon of cyberchondria, which potentially causes individuals to avoid getting tested and quarantined directly upon experiencing symptoms for fear of losing their salaries or jobs. We utilize Google Trends data to predict future disease statistics, like the pandemic's impact on human activities and health-related issues in India. By means of a bootstrapped Pearson correlation, a time-lead correlation, and a quantile regression, we found a strong relationship between Google Trend searches and COVID-19 cases. Contextualizing the second COVID-19 wave in India through the lenses of cyberchondria and protection motivation theory, our article notes that, when people develop COVID-19 symptoms, they turn to Google for confirmation and treatment, rather than getting themselves checked early, only getting medically tested, and treated when their health deteriorates. At that stage, given the patients’ critical conditions, hospitalization is the only option. This places an unsustainable burden on hospitals, resulting in capacity constraints and increased mortality rates. We suggest using Google Trends data to forecast COVID-19 waves and mobilize the health infrastructure to save lives and facilitate friction-free growth.