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• W3136600992 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, Nonnegative 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." @default.
• W3136600992 created "2021-03-29" @default.
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• W3136600992 date "2020-12-10" @default.
• W3136600992 modified "2023-09-27" @default.
• W3136600992 title "Google Trends Analysis of COVID-19 Pandemic" @default.
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• W3136600992 doi "https://doi.org/10.1109/bigdata50022.2020.9377852" @default.
• W3136600992 hasPublicationYear "2020" @default.
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