On the predictability of COVID-19 in USA: A Google Trends analysis

DOI: https://doi.org/10.21203/rs.3.rs-27189/v1


During the difficult times that the world is facing due to the COVID-19 pandemic that has already had severe consequences in all aspects of our lives, it is imperative to explore novel approaches of monitoring and forecasting the regional outbreaks as they happen or even before they do. In this paper, the first approach of exploring the role of Google query data in the predictability of COVID-19 in the US at both national and state level is presented. The results indicate that Google Trends correlate with COVID-19 data, while the estimated models exhibit strong predictability of COVID-19. In line with previous work that has argued on the value of online real-time data in the monitoring and forecasting epidemics and outbreaks, it is evident that such infodemiology approaches can assist public health policy makers, in order to address the most crucial issue; that of flattening the curve, allocating health resources, and increasing the effectiveness and preparedness of the respective health care systems.

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