Google Trends Analysis of COVID-19

TL;DR

This paper investigates the correlation between Google search trends and COVID-19 case numbers, demonstrating that search data can effectively predict case growth using machine learning, especially deep neural networks.

Contribution

It introduces a hybrid forecasting approach combining Google Trends data with machine learning techniques, highlighting deep learning's superior performance in COVID-19 case prediction.

Findings

Google search trends are highly correlated with COVID-19 confirmed cases.

Deep neural networks outperform other models in forecasting accuracy.

Hybrid data improves prediction reliability.

Abstract

The World Health Organization (WHO) announced that COVID-19 was a pandemic disease on the 11th of March as there were 118K cases in several countries and territories. Numerous researchers worked on forecasting the number of confirmed cases since anticipating the growth of the cases helps governments adopting knotty decisions to ease the lockdowns orders for their countries. These orders help several people who have lost their jobs and support gravely impacted businesses. Our research aims to investigate the relation between Google search trends and the spreading of the novel coronavirus (COVID-19) over countries worldwide, to predict the number of cases. We perform a correlation analysis on the keywords of the related Google search trends according to the number of confirmed cases reported by the WHO. After that, we applied several machine learning techniques (Multiple Linear Regression, Non-negative Integer Regression, Deep Neural Network), to forecast the number of confirmed cases globally based on historical data as well as the hybrid data (Google search trends). Our results show that Google search trends are highly associated with the number of reported confirmed cases, where the Deep Learning approach outperforms other forecasting techniques. We believe that it is not only a promising approach for forecasting the confirmed cases of COVID-19, but also for similar forecasting problems that are associated with the related Google trends.