Google Searches and COVID-19 Cases in Saudi Arabia: A Correlation Study
Btool Hamoui, Abdulaziz Alashaikh, Eisa Alanazi

TL;DR
This study explores the feasibility of using Google Trends data related to COVID-19 symptoms to monitor and predict the outbreak in Saudi Arabia, demonstrating significant correlations with actual case data.
Contribution
It introduces a digital surveillance model leveraging Google search data for COVID-19 monitoring, which is a novel approach in the context of Saudi Arabia.
Findings
Google searches for symptoms like Loss of Smell and Taste strongly correlate with COVID-19 case numbers.
Google Trends data can serve as a supplementary tool for pandemic surveillance.
Symptoms related to COVID-19 show significant search patterns that align with case trends.
Abstract
Background: The outbreak of the new coronavirus disease (COVID-19) has affected human life to a great extent on a worldwide scale. During the coronavirus pandemic, public health professionals at the early outbreak faced an extraordinary challenge to track and quantify the spread of disease. Objective: To investigate whether a digital surveillance model using google trends (GT) is feasible to monitor the outbreak of coronavirus in the Kingdom of Saudi Arabia. Methods: We retrieve GT data using ten common COVID-19 symptoms related keywords from March 2, 2020, to October 31, 2020. Spearman correlation were performed to determine the correlation between COVID-19 cases and the Google search terms. Results: GT data related to Cough and Sore Throat were the most searched symptoms by the Internet users in Saudi Arabia. The highest daily correlation found with the Loss of Smell followed by Loss…
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Taxonomy
TopicsData-Driven Disease Surveillance · COVID-19 epidemiological studies · Influenza Virus Research Studies
