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Abdulrahman M Jabour,1 Joe Varghese,1 Ahmed H Damad,2 Khalid Y Ghailan,3 Asim M Mehmood1 1Health Informatics Department, Faculty of Public Health and Tropical Medicine, Jazan University, Jazan, Saudi Arabia; 2Quality & Patient Safety Department, King Fahd Central Hospital - Jazan, Jazan, Saudi Arabia; 3Epidemiology Department, Faculty of Public Health and Tropical Medicine, Jazan University, Jazan, Saudi ArabiaCorrespondence: Abdulrahman M JabourHealth Informatics Department, Faculty of Public Health and Tropical Medicine, Jazan University, Jazan, Saudi ArabiaTel +966-17329500 Ex 5545Email ajabour@jazanu.edu.saIntroduction: Many studies have explored social media and users search activities such as Google Trends to predict and detect influenza activities. Studies that examined Google Trends correlation with the actual hospital influenza cases were conducted in non-tropical regions that have clearly defined seasons. Tropical areas are known for having less-defined seasonality and the extent of Google Trends concordance with actual influenza cases is unknown for these areas. The goal of this study is to compare Google Trends with hospital cases in tropical regions.Methods: We analyzed 48,263 influenza cases in the time period of 2010 to 2019. The cases were retrieved from central hospital medical records in tropical regions using the corresponding codes for influenza ICD-10 AM. Cases from the medical records were compared with Google Trends to determine trends, seasonality, and correlation.Results: Graphically, there were some similar areas of the trend, but cross-correlation analysis did not show any significant correlation between hospital and Google Trends with a maximum correlation rate of 0.300. Seasonality analysis showed a clear pattern that peaked around November in Google Trends while hospital data showed less defined seasonality with a smaller peak occurring at the end of December and beginning of January.Conclusion: Based on the results, there is a weak correlation between Google Trends and hospital data. More innovative methods are emerging to predict influenza activity using social media and user search data and further study is needed to examine the concurrent trends derived using these methods across regions that have different humidity levels and temperatures.Keywords: influenza, predictive, surveillance, data quality, disease outbreaks, big data, epidemiological monitoring