  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.
